Global Inventory Research Articles

Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution

Abstract. Reducing oil and gas methane emissions is crucially important for limiting the rate of human-induced climate warming. As the capacity of multi-scale measurements of global oil and gas methane emissions has advanced in recent years, including the emerging ecosystem of satellite and airborne remote sensing platforms, a clear need for an openly accessible and regularly updated global inventory of oil and gas infrastructure has emerged as an important tool for characterizing and tracking methane emission sources. In this study, we develop a spatially explicit database of global oil and gas infrastructure, focusing on the acquisition, curation, and integration of public-domain geospatial datasets reported by official government sources and by industry, academic research institutions, and other non-government entities. We focus on the major oil and gas facility types that are key sources of measured methane emissions, including production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. The first version of this global geospatial database (Oil and Gas Infrastructure Mapping database, OGIM_v1) contains a total of ∼ 6 million features, including 2.6 million point locations of major oil and gas facility types and over 2.6×106 km of pipelines globally. For each facility record, we include key attributes – such as facility type, operational status, oil and gas production and capacity information, operator names, and installation dates – which enable detailed methane source assessment and attribution analytics. Using the OGIM database, we demonstrate facility-level source attribution for multiple airborne remote-sensing-detected methane point sources from the Permian Basin, which is the largest oil-producing basin in the United States. In addition to source attribution, we present other major applications of this oil and gas infrastructure database in relation to methane emission assessment, including the development of an improved bottom-up methane emission inventory at high resolution (1 km × 1 km). We also discuss the tracking of changes in basin-level oil and gas activity and the development of policy-relevant analytics and insights for targeted methane mitigation. This work and the OGIM database, which we anticipate updating on a regular cadence, help fulfill a crucial oil and gas geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution. OGIM_v1 is publicly available at https://doi.org/10.5281/zenodo.7466757 (Omara et al., 2022a).

An exploratory approach to estimate point emission sources

The current atmospheric emission inventories do not fully meet the spatial and temporal resolution requirements of air quality modelling applications. Considering Portugal as a case study and focusing on combustion point emission sources (i.e., public power, refineries, manufacturers, and construction activities), this work proposes a methodological approach and dataset to estimate anthropogenic emissions suitable for different spatial scales (from regional to local). The obtained results were similar to the annual values reported by the Portuguese Environment Agency with the maximum emissions being estimated for manufacturing and construction activities. No significant differences were recorded between the temporal profiles developed in this and previous studies. However, the country-specific proxies from the developed database allowed us to better represent the temporal and spatial patterns of the Portuguese atmospheric emissions. The combination of the BigAir database with a comprehensive and standardized approach could help policymakers define mitigation and/or plan measures to reduce emissions from point sources, support countries worldwide (with a lack of data) to develop high-resolution emission inventories, and improve the current global and European inventories.

Assessment of Burned Areas during the Pantanal Fire Crisis in 2020 Using Sentinel-2 Images

The Pantanal biome—a tropical wetland area—has been suffering a prolonged drought that started in 2019 and peaked in 2020. This favored the occurrence of natural disasters and led to the 2020 Pantanal fire crisis. The purpose of this work was to map the burned area’s extent during this crisis in the Brazilian portion of the Pantanal biome using Sentinel-2 MSI images. The classification of the burned areas was performed using a machine learning algorithm (Random Forest) in the Google Earth Engine platform. Input variables in the algorithm were the percentiles 10, 25, 50, 75, and 90 of monthly (July to December) mosaics of the shade fraction, NDVI, and NBR images derived from Sentinel-2 MSI images. The results showed an overall accuracy of 95.9% and an estimate of 44,998 km2 burned in the Brazilian portion of the Pantanal, which resulted in severe ecosystem destruction and biodiversity loss in this biome. The burned area estimated in this work was higher than those estimated by the MCD64A1 (35,837 km2), Fire_cci (36,017 km2), GABAM (14,307 km2), and MapBiomas Fogo (23,372 km2) burned area products, which presented lower accuracies. These differences can be explained by the distinct datasets and methods used to obtain those estimates. The proposed approach based on Sentinel-2 images can potentially refine the burned area’s estimation at a regional scale and, consequently, improve the estimate of trace gases and aerosols associated with biomass burning, where global biomass burning inventories are widely known for having biases at a regional scale. Our study brings to light the necessity of developing approaches that aim to improve data and theory about the impacts of fire in regions critically sensitive to climate change, such as the Pantanal, in order to improve Earth systems models that forecast wetland–atmosphere interactions, and the role of these fires on current and future climate change over these regions.

Ground-based and AIRS carbon monoxide behavior at El Arenosillo observatory (Southwestern Europe)

Carbon monoxide (CO) observations registered at El Arenosillo observatory (Southwestern Europe), during three years (2019–2022) were used to explore levels, temporal variations and patterns. The seasonal mean hourly oscillated between 127 ± 21 and 97 ± 14 ppb, with extreme peaks measured under wildfire plume arrival. A remarkable monthly evolution was observed with peaks in cold months (135 ± 20 ppb in January) and minimum in summer (90 ± 11 ppb in June). Seasonal daily patterns showed a different nocturnal behavior depending on the season, with a clear maximum at 7:00–9:00 UTC, a decrease during diurnal time, and a minimum at 17:00–18:00 UTC. To determine the potential trends from 2002 to 2022, the observations of the AIRS (Atmospheric Infrared Sounder) instrument and the emissions from the global emission inventory of CAMS (Copernicus Atmosphere Monitoring Service) were used. A downward trend for emissions of −31% decade−1 was found, whereas the decrease from AIRS was −9.8% decade−1. The effort to reduce CO emissions is being countered by other processes. The CO behavior was investigated under weather conditions governed by the synoptic and mesoscale processes. Background levels (∼90 ppb) were obtained under Atlantic airflows, while an increase of ∼40% was observed with the impact of urban plumes from continental areas. CO even lower than 90 ppb was obtained under non-pure sea-land breezes, whereas, with pure breezes, reached peaks above 120 ppb, affecting the atmosphere at the surface and up to 1–1.2 km in the air. The extreme level was registered under wildfire plume arrival, with peaks exceeding 350 ppb. Climate change is modifying weather patterns and modulating breezes; in addition, it is also increasing the frequency and intensity of wildfires. These processes could be counteracting the efforts carried out to reduce emissions.

An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers

Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed. The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89.2%–93.7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km2) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.

The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies

Abstract. This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the global and regional atmospheric modelling community's need of a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3, an ad hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and South Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO2, NOx, CO, non-methane volatile organic compounds (NMVOCs), NH3, PM10, PM2.5, black carbon (BC), and organic carbon (OC), with a spatial resolution of 0.1∘ × 0.1∘ and time intervals of months and years, covering the period 2000–2018 (https://doi.org/10.5281/zenodo.7516361, Crippa, 2023, https://edgar.jrc.ec.europa.eu/dataset_htap_v3, last access: June 2023). The emissions are further disaggregated into 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past 2 decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policymaking, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Implications of differences between recent anthropogenic aerosol emission inventories for diagnosed AOD and radiative forcing from 1990 to 2019

Abstract. This study focuses on implications of differences between recent global emissions inventories for simulated trends in anthropogenic aerosol abundances and radiative forcing (RF) over the 1990–2019 period. We use the ECLIPSE version 6 (ECLv6) and CEDS year 2021 release (CEDS21) as input to the chemical transport model OsloCTM3 and compare the resulting aerosol evolution to corresponding results derived with the first CEDS release, as well as to observed trends in regional and global aerosol optical depth (AOD). Using CEDS21 and ECLv6 results in a 3 % and 6 % lower global mean AOD compared to CEDS in 2014, primarily driven by differences over China and India, where the area average AOD is up to 30 % lower. These differences are considerably larger than the satellite-derived interannual variability in AOD. A negative linear trend over 2005–2017 in global AOD following changes in anthropogenic emissions is found with all three inventories but is markedly stronger with CEDS21 and ECLv6. Furthermore, we confirm that the model better captures the sign and strength of the observed AOD trend over China with CEDS21 and ECLv6 compared to using CEDS, while the opposite is the case for South Asia. We estimate a net global mean aerosol-induced RF in 2014 relative to 1990 of 0.08 W m−2 for CEDS21 and 0.12 W m−2 for ECLv6, compared to 0.03 W m−2 with CEDS. Using CEDS21, we also estimate the RF in 2019 relative to 1990 to be 0.10 W m−2, reflecting the continuing decreasing trend in aerosol loads post-2014. Our results facilitate more rigorous comparison between existing and upcoming studies of climate and health effects of aerosols using different emission inventories.

Carbon Life Cycle Assessment and Costing of Building Integrated Photovoltaic Systems for Deep Low-Carbon Renovation

Building integrated photovoltaic (BIPV) systems can achieve high yields through high percentages of building envelope surface coverage associated with material savings by substituting conventional building envelope components and avoiding land-use change to install open-land PV installations. This article discusses the life cycle assessment (LCA) and the life cycle costing (LCC) of BIPV systems in timber-hybrid building extensions and envelope renovation systems of three exemplary buildings in the Republic of Korea: apartment, mixed-use commercial/industrial, and low-rise multi-unit residential. The BIPV system’s electricity production was quantified with simulation tools. Minimum and average carbon LCAs were calculated using a global product inventory database for 50 years. Greenhouse gas (GHG) emission savings by substituting conventional energy supplies were calculated based on the associated primary energy demands. LCC calculations were based on international datasets for BIPV LCC for 25 and 50 years. As a result, the BIPV system-associated GHG emissions can be decreased by up to 30% with a payback time of 12 (apartment) to 41 (mixed-use building) years for buildings with full PV coverage. The positive cumulative net present value (NPV) for both LCC scenarios encourages economic investments in building renovations with BIPV systems.

Development of an emission estimation method with satellite observations for significant forest fires and comparison with global fire emission inventories: Application to catastrophic fires of summer 2021 over the Eastern Mediterranean

In the past few decades, forest fires have increased in number and severity, especially in the Mediterranean regions of Türkiye and Greece, where significant fires caused damage to thousands of hectares of land as well as wildlife. The main objective of the present study is to develop an emission estimation method with satellite-based burned area data from significant forest fire events in the Eastern Mediterranean in July–August 2021. In the first stage of this study, pre-fire and post-fire images of the study area acquired by the Sentinel-2 satellite were processed to calculate the normalized burn rate difference index (dNBR). Then, CORINE Land Cover (CLC) data were used for detecting land cover classes in the burned areas. Atmospheric emissions of NOx, CO, SO2, total suspended particulate matter (TSP), particulate matter with diameters that are equal to or smaller than 2.5 μm (PM2.5), and black carbon (BC) were estimated using the EMEP/EEA Tier 2 - technology-specific approach method, in which burned area maps were retrieved using Sentinel-2 imageries and later combined with land cover type and burning efficiency to estimate the quantity of burning biomass emissions. Emission factors were then used to estimate the fires' trace gas and aerosol emissions. The results showed that the highest burned areas were found in the western Mediterranean region in Türkiye and Central Greece (⁓50,000 ha). The atmospheric emissions from these fires were calculated to be similar in both countries. Furthermore, emission amounts were compared with three different global fire emission inventories including GFAS, GFED, and FINN. The emissions obtained from the GFAS database were the highest emissions of the four emission estimation approaches and our estimated emissions were close to the GFAS. Emissions calculated from the other two databases (FINN and GFED) mostly provided underestimated emissions. The emission uncertainties in this study mainly originated from assumptions regarding the inclusion of burned area efficiency in emission calculations, the landcover dataset, and the emission factors used. Overall, this study is considered a new approach to emission calculations using Sentinel-2 data. This research provides further insight into the use of Sentinel-2 data in emission calculation applications at the local to regional scales.

Landslide detection in real-time social media image streams

Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.

Seasonal transition of Black carbon aerosols over Qinghai-Tibet Plateau: Simulations with WRF-Chem

Black carbon (BC) particles over the Qinghai-Tibet Plateau (Q-TP), whether suspended in the air or deposited on snow, can affect snow melt and accelerate glacier retreat. Since BC over the Q-TP may originate from different regions, it is necessary to understand the transport mechanism of BC and then provide concentrations and spatial-temporal distributions of BC over Q-TP, in order to study its radiative and climatic effects. In this study, Weather Research and Forecasting model with Chemistry (WRF-Chem), with MIX emission inventory (a mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects), was used to study the BC transport from South Asia, one of the major BC source regions, to Q-TP in April (pre-monsoon season), July (summer monsoon season) and December (winter monsoon season). The model's ability to simulate the BC transport from South Asia to the Q-TP was firstly evaluate using various reanalysis data, including 2 m temperature, precipitation, 10 m wind speed and direction and BC concentration at surface. The simulated meteorological fields and seasonal distribution and variation of BC are generally in good agreement with reanalysis data. The model showed good performance in simulating transport of BC as well. Our study showed that during pre-monsoon season, BC aerosols accumulated in the foothills of the Himalayas and were transported to southern Q-TP and even Southeast Asia via dry and wet deposition and westerly winds below 200hpa. Although the cyclonic airflow, which was favorable to transport, prevailed in South Asia, slight BC aerosols would be transported to Q-TP through wet deposition due to the efficient wet scavenging by abundant precipitation during summer monsoon season. During winter monsoon season, the prevailing westerly winds at 500hpa would transport BC aerosols from South Asia to Q-TP by dry deposition. A sensitivity simulation using PKU emission inventory (Peking University's global BC emission inventory) was then conducted to investigate the impact of different emission inventory on the modeling results. It is shown that the simulations using the different BC emission inventories overall did not have significant impacts on spatial distributions of surface concentration and column burden during three studies seasons. However, the simulated BC concentrations and burden based on PKU were overall higher than that based on MIX, that is, main differences were found in the southern part of Q-TP (i.e. adjacent to South Asia), especially during pre-monsoon and winter monsoon seasons. The differences of mean BC surface concentration ranging from 0.1 to 1 μg/m3, and burden ranging from 0.1 to 1 mg/m2 due to both higher dry and wet deposition (0.1–2 μg/m2/s for dry deposition flux, and 0.1–3 μg/m2/s for wet deposition flux) during pre-monsoon. During winter monsoon seasons, relatively large differences (0–3 μg/m3 for concentration, and 0–2 mg/m2 for burden, and 0.1–3 μg/m2/s for dry deposition flux) were found in southern Q-TP.

Drivers of variability in Blue Carbon stocks and burial rates across European estuarine habitats

The implementation of climate change mitigation strategies based on the conservation and restoration of Blue Carbon ecosystems requires a deep understanding of the magnitude and variability in organic carbon (Corg) storage across and within these ecosystems. This study explored the variability in soil Corg stocks and burial rates across and within intertidal estuarine habitats of the Atlantic European coast and its relation to biotic and abiotic drivers. A total of 136 soil cores were collected across saltmarshes located at different tidal zones (high marsh, N = 45; low marsh, N = 30), seagrass meadows (N = 17) and tidal flats (N = 44), and from the inner to the outer sections of five estuaries characterized by different basin land uses. Soil Corg stocks were higher in high-marsh communities (65 ± 3 Mg ha−1) than in low-marsh communities (38 ± 3 Mg ha−1), seagrass meadows (40 ± 5 Mg ha−1) and unvegetated tidal flats (46 ± 3 Mg ha−1) whereas Corg burial rates also tended to be higher in high marshes (62 ± 13 g m−2 y−1) compared to low marshes (43 ± 15 g m−2 y−1) and tidal flats (35 ± 9 g m−2 y−1). Soil Corg stocks and burial rates decreased from inner to outer estuarine sections in most estuaries reflecting the decrease in the river influence towards the estuary mouth. Higher soil Corg stocks were related to higher content of silt and clay and higher proportion of forest and natural land within the river basin, pointing at new opportunities for protecting coastal natural carbon sinks based on the conservation and restoration of upland ecosystems. Our study contributes to the global inventory of Blue Carbon by adding data from unexplored regions and habitats in Europe, and by identifying drivers of variability across and within estuaries.

Regulation of plant carbon assimilation metabolism by post-translational modifications.

The flexibility of plant growth, development and stress responses is choreographed by an intricate network of signaling cascades and genetic programs. However, it is metabolism that ultimately executes these programs through the selective delivery of specific building blocks and energy. Photosynthetic carbon fixation is the central pillar of the plant metabolic network, the functioning of which is conditioned by environmental fluctuations. Hence, regulation of carbon assimilation metabolism must be particularly versatile and rapid to maintain efficiency and avoid dysfunction. While changes in gene expression can adjust the global inventory and abundance of relevant proteins, their specific characteristics are dynamically altered at the post-translational level. Here we highlight studies that show the extent of the regulatory impact by post-translational modification (PTM) on carbon assimilation metabolism. We focus on examples for which there has been empirical evidence of functional changes associated with a PTM, rather than just the occurrence of PTMs at specific sites in proteins, as regularly detected in proteomic studies. The examples indicate that we are only at the beginning of deciphering the PTM-based regulatory network that operates in plant cells. However, it is becoming increasingly clear that targeted exploitation of PTM engineering has the potential to control the metabolic flux landscape as a prerequisite for increasing crop yields, modifying metabolite composition, optimizing stress tolerance, and even executing novel growth and developmental programs.

Greenhouse gas emissions and environmental drivers in different natural wetland regions of China

Wetlands sequestrate carbon at the highest rate than any other ecosystems on Earth. However, the spatial and temporal dynamics of GHGs emissions from the wetland ecosystems in China are still elusive. We synthesized 166 publications that contain 462 in situ measurements of GHGs emissions from the natural wetlands in China, and further analyzed the variability and the drivers of GHGs emissions in eight subdivisions of China's wetlands. The results show that the current studies are mainly concentrated in the estuaries, Sanjiang Plain, and Zoige wetlands. The average CO2 emissions, CH4 fluxes and N2O fluxes from Chinese wetlands were 218.84 mg·m−2·h−1, 1.95 mg·m−2·h−1 and 5.8 × 10−2 mg·m−2·h−1, respectively. The global warming potential (GWP) of China's wetlands was estimated to be 1881.36 TgCO2-eq·yr−1, with CO2 emissions contributing more than 65% to the GWP value. The combined GWP values of Qinghai-Tibet Plateau wetlands, coastal wetlands and northeastern wetlands account for 84.8% of GWP of China's wetlands. Correlation analysis showed that CO2 emissions increased with the increasing mean annual temperature, elevation, annual rainfall, and wetland water level, but decreased with soil pH. CH4 fluxes increased with the mean annual temperature and soil water content but decreased with the redox potential. This study analyzed the drivers of GHGs emissions from wetland ecosystems at the national scale, and GWP values of eight wetland subregions of China were comprehensively assessed. Our results are potentially useful for the global GHGs inventory, and can help assess the response of GHGs emissions of wetland ecosystem to environmental and climate change.

Global vegetation greenness interannual variability and its evolvement in recent decades

The interannual variability (IAV) of global vegetation greenness needs careful assessment as it relates to the stability of the climate, conservation of biodiversity, sustainable ecosystem services, and global food security. Here, we investigated the spatial feature and temporal evolvement of global vegetation greenness interannual variability from 1982 to 2015 using the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index third generation (NDVI3g) data. Generally, regions with herbaceous and short woody plants had larger IAV of vegetation greenness than those with tall woody biome types (7.9% versus 2.9%). On average, all the biomes displayed increasing IAV of vegetation greenness from 1982 to 2015, with notable increases over northern high latitudes (0.135%/year), Eastern Europe (0.037%//year), and Central Australia (0.231%/year). Croplands in China and India experienced decreasing IAV of vegetation greenness (–0.037%/year for China and –0.004%/year for India). The changing IAV of vegetation greenness had implications for climatic, environmental, and anthropogenic changes that influence vegetation dynamics. Some note-worthy factors include climate warming, the CO2 fertilization effect, agricultural practice improvement, cropland abandonment, and China’s Grain-for-Green Program.

Comparing Urban Anthropogenic NMVOC Measurements With Representation in Emission Inventories—A Global Perspective

AbstractEmission inventories are a critical basis for air quality and climate modeling, as well as policy decisions. Non‐methane volatile organic compounds (NMVOCs) are key precursor compounds in ozone and secondary organic aerosol formation. Accurately representing NMVOCs in emission inventories is crucial for understanding atmospheric chemistry, the impact of policy measures, and climate projections. Improving NMVOC representation in emission inventories is fraught with challenges, ranging from the lack of (long‐term) NMVOC measurements, limited efforts in updating emission factors, to the diversity of NMVOC species reactivity. Here we take an initial step to evaluate the representation of urban NMVOC speciation in an emission inventory (EDGARv4.3.2 and EDGARv6.1) at the global level. To compare the urban measurements of NMVOCs to the emission inventory estimates, ratios of individual NMVOCs to acetylene are used. Owing to limitations in measurement data and grouping of NMVOCs in emission inventories, the comparison includes only a limited number of alkanes, alkenes, and aromatics. Results show little to no agreement between the ratios in the observations and those in the global emission inventory for the species compared (r2 0.01–0.20). This could be related to incorrect speciation profiles and/or spatial allocation of NMVOCs to urban areas. Regional emission inventories show better agreement among the ratios (r2 0.43–0.70). The inclusion of oxygenated species in NMVOC measurements, as well as greater global coverage of measurements could improve representation of NMVOC species in emission inventories, and a mosaic of regional inventories may be a better approach.

Temporal and Spatial Change in Vegetation and Its Interaction with Climate Change in Argentina from 1982 to 2015

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change.

Ubiquity of Anthropogenic Terpenoids in Cities Worldwide: Emission Ratios, Emission Quantification and Implications for Urban Atmospheric Chemistry

AbstractTerpenoids (isoprene and monoterpenes) are highly reactive Volatile Organic Compounds (VOCs) known for decades for their biogenic origin. Here, we discuss the nature and magnitude of their anthropogenic emissions. We compiled and re‐analyzed 14 data sets of in situ VOC observations collected over the last decade in contrasting urban areas from mid‐latitudes to subtropical regions. We show the systematic presence of anthropogenic terpenoids in urban ambient air with clear covariations with anthropogenic compounds (R2 > 0.50) even during mid‐latitude winters. Despite the emerging importance of monoterpene emissions from consumer products in North American cities, there is some evidence of monoterpene emissions from tailpipe exhaust in cities of the developing world. The traffic‐related fraction of monoterpenes is estimated and can account for up to 40% of their ambient levels. The anthropogenic emission ratios (ER) of some terpenoids (isoprene, α‐pinene and limonene) are estimated and spatially compared. The anthropogenic emissions of terpenoids are indirectly estimated from those ER combined to regional and global emission inventories (CAMS‐GLOB‐ANT_v4.2) at urban and country scale focusing on France, Lebanon, and Vietnam. Those anthropogenic emissions do not represent more than 3% of other anthropogenic VOC emissions. However, they dominate by one to three orders of magnitude the reactivity of other anthropogenic VOCs regarding NO3 oxidation and ozonolysis. This study raises two questions which need further investigations in the future: (a) the significance of terpenoid emissions from traffic, especially in urban areas of the developing world and (b) the role of anthropogenic terpenoids in nighttime and wintertime atmospheric chemistry at mid‐latitudes.

Estimating enhancement ratios of nitrogen dioxide, carbon monoxide and carbon dioxide using satellite observations

Abstract. Using co-located space-based measurements of carbon dioxide (CO2) from the Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 (OCO-2/3) and carbon monoxide (CO) and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI), we calculate total column enhancements for observations influenced by anthropogenic emissions from urban regions relative to clean background values. We apply this method to observations taken over or downwind of 27 large (population of >1 million) urban areas from around the world. Enhancement ratios between species are calculated and compared to emissions ratios derived from four globally gridded anthropogenic emissions inventories. We find that these global inventories underestimate CO emissions in many North American and European cities relative to our observed enhancement ratios, while smaller differences were found for NO2 emissions. We further demonstrate that the calculation and intercomparison of enhancement ratios of multiple tracers can help to identify the underlying biases leading to disagreement between observations and inventories. Additionally, we use high-resolution CO2 inventories for two cities (Los Angeles and Indianapolis) to estimate emissions of CO and NO2 using our calculated enhancement ratios and find good agreement with both a previous modelling study for the megacity of Los Angeles and California Air Resources Board (CARB) inventory estimates.

Accounting of the Use and Emissions of Polychlorinated Biphenyl Compounds (PCBs) in India, 1951-2100.

Polychlorinated biphenyl compounds (PCBs) are highly toxic organic chemicals still prevalent in the environment. While global inventories of the use and emissions of PCBs have been developed, estimates for individual countries determined using bottom-up approaches are few and often show different trends from the global inventory. Here, we determine the past, present, and future consumption and emissions of PCBs in India. A mass balance model was used to estimate middle (low-high) emissions in the period 1950-2100. Up to 7296 tonnes of PCBs have been used in transformers. PCBs imported as wastes are estimated to be approximately 5000 (2400-9100) tonnes. Total emissions from the use and disposal of transformers, industrial processes, and imported waste disposal are estimated to become 13 (0.1-537) tonnes, 89.26 (0.5-178) tonnes, 63 (3-910) tonnes, respectively, in the period 1950-2100. Congener-specific emissions are relatively high for low-chlorinated PCBs (-8, 18, 28, 31, 52, 101, 110, 118, 153, range: 0.1-118 tonnes). We find that industrial emissions are becoming important sources of PCBs and may become predominant, depending on emission scenarios.