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.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry

Volatile organic compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). In this respect, long-term datasets of in situ atmospheric measurements are crucial for characterizing the variability of atmospheric chemical composition, its sources, and trends. The ongoing establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementation of the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOx), and aerosols have been carried out since 2012 at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory, located in the Paris region, France. Within the last decade, VOC measurements were conducted offline at SIRTA, until the implementation of real-time monitoring which started in January 2020 using a proton-transfer-reaction quadrupole mass spectrometer (PTR-Q-MS).The dataset acquired during the first 2 years of online VOC measurements provides insights into their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOx, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtained results. Results also include insights into VOC main sources and the influence of meteorological conditions and air mass origin on their levels in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably saw a quasi-total lockdown in France in spring and a lighter one in autumn. Therefore, the focus is placed on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. A comparison with gas chromatography data from the Paris city centre consolidates the regional representativity of the SIRTA station for benzene, while differences are observed for shorter-lived compounds with a notable impact of their local sources. This dataset could be further used as input for atmospheric models and can be found at 10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research

The successes of YOPP from the presentations and keynote presentations included * a better understanding of the impact of key polar measurements (radiosondes and space-based instruments such as microwave radiometers), and recent advancements in the current NWP observing system, achieved through coordinated OSEs in both polar regions (e.g., Sandu et al. 2021);* enhanced understanding of the linkages between Arctic and midlatitude weather (e.g., Day et al. 2019);* advancements in the atmosphere–ocean–sea ice and atmosphere–land–cryosphere coupling in NWP, and in assessing and recognizing the added value of coupling in Earth system models (e.g., Bauer et al. 2016);* deployment of tailored polar observation campaigns to address yet-unresolved polar processes (e.g., Renfrew et al. 2019);* progress in verification and forecasting techniques for sea ice, including a novel headline score (e.g., Goessling and Jung 2018);* advances in process understanding and process-based evaluation with the establishment of the YOPPsiteMIP framework and tools (Svensson 2020);* better understanding of emerging societal and stakeholder needs in the Arctic and Antarctic (e.g., Dawson et al. 2017);and * innovative transdisciplinary methodologies for coproducing salient information services for various user groups (Jeuring and Lamers 2021). The YOPP Final Summit identified a number of areas worthy of prioritized research in the area of environmental prediction and services for the polar regions: * coupled atmosphere, sea ice, and ocean models with an emphasis on advanced parameterizations and enhanced resolution at which critical phenomena start to be resolved (e.g., ocean eddies);* improved definition and representation of stable boundary layer processes, including mixed-phase clouds and aerosols;incorporation of wave–ice–ocean interactions;* radiance assimilation over sea ice, land ice, and ice sheets;understanding of linkages between polar regions and lower latitudes from a prediction perspective;* exploring the limits of predictability of the atmosphere–cryosphere–ocean system;* an examination of the observational representativeness over land, sea ice, and ocean;better representation of the hydrological cycle;and * transdisciplinary work with the social science community around the use of forecasting services and operational decision-making to name but a few. The presentations and discussions at the YOPP Final Summit identified the major legacy elements of YOPP: the YOPPsiteMIP approach to enable easy comparison of collocated multivariate model and observational outputs with the aim of enhancing process understanding, the development of an international and multi-institutional community across many disciplines investigating aspects of polar prediction and services, the YOPP Data Portal3 (https://yopp.met.no/), and the education and training delivered to early-career polar researchers. Next steps Logistical issues, the COVID-19 pandemic, but also new scientific questions (e.g., the value of targeted observations in the Southern Hemisphere), as well as technical issues emerging toward the end of the YOPP Consolidation Phase, resulted in the decision to continue the following three YOPP activities to the end of 2023: (i) YOPP Southern Hemisphere (YOPP-SH);(ii) Model Intercomparison and Improvement Project (MIIP);of which YOPPSiteMIP is a critical element;and (iii) the Societal, Economics and Research Applications (PPP-SERA) Task Team.

Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments

Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Impact of COVID‐19‐Related Air Traffic Reductions on the Coverage and Radiative Effects of Linear Persistent Contrails Over Conterminous United States and Surrounding Oceanic Routes

The radiative effects of the large‐scale air traffic slowdown during April and May 2020 due to the international response to the COVID‐19 pandemic are estimated by comparing the coverage (CC), optical properties, and radiative forcing of persistent linear contrails over the conterminous United States and two surrounding oceanic air corridors during the slowdown period and a similar baseline period during 2018 and 2019 when air traffic was unrestricted. The detected CC during the slowdown period decreased by an area‐averaged mean of 41% for the three analysis boxes. The retrieved contrail optical properties were mostly similar for both periods. Total shortwave contrail radiative forcings (CRFs) during the slowdown were 34% and 42% smaller for Terra and Aqua, respectively. The corresponding differences for longwave CRF were 33% for Terra and 40% for Aqua. To account for the impact of any changes in the atmospheric environment between baseline and slowdown periods on detected CC amounts, the contrail formation potential (CFP) was computed from reanalysis data. In addition, a filtered CFP (fCFP) was also developed to account for factors that may affect contrail formation and visibility of persistent contrails in satellite imagery. The CFP and fCFP were combined with air traffic data to create empirical models that estimated CC during the baseline and slowdown periods and were compared to the detected CC. The models confirm that decreases in CC and radiative forcing during the slowdown period were mostly due to the reduction in air traffic, and partly due to environmental changes.Alternate :Plain Language SummaryContrails produced by aircraft flying in cold but humid air both warm the atmosphere by reducing infrared radiation emitted back into space and cool it by increasing reflected sunlight. Due to the decrease in air traffic during the first months of the COVID pandemic, fewer satellite‐detectable contrails were produced compared to pre‐pandemic times, and thus the radiative effects of contrails were also diminished. But changes in the overall temperature and humidity at aircraft cruise altitudes also affect contrail formation and might explain at least some of the observed decrease in contrail coverage during April and May 2020. Analysis of satellite imagery showed that the thickness and ice‐crystal size of the contrails during the COVID period did not change much from pre‐pandemic contrails. The regional contrail coverage was accurately simulated from a combination of the estimated air traffic activity at cruise altitude and the probable frequency of when atmospheric conditions were favorable for contrail formation. This simulation confirms that most of the decrease in contrails and their radiative effects during the COVID‐related slowdown period were due to the reduction in air traffic, and to a lesser extent to changes in temperature and humidity at cruise altitude during April and May 2020.

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 .

Two years of Volatile Organic Compounds online in-situ measurements at SIRTA (Paris region, France) using Proton-Transfer-Reaction Mass Spectrometry

Volatile Organic Compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry, as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). To this respect, long-term datasets of in-situ atmospheric measurements are crucial to characterize the variability of atmospheric chemical composition, its sources and trends. The on-going establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementing the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOX) and aerosols have been carried out since 2012 at the SIRTA observatory, located in the Paris region, France. Within the last decade, VOC measurements have been conducted offline at SIRTA, until the implementation of a real-time monitoring which started in January 2020, using a Proton-Transfer-Reaction Quadrupole Mass-Spectrometer (PTR-Q-MS). The dataset acquired during the first two years of online VOC measurements provides insights on their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOX, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtain results. Results also include insights on VOC's main sources and the influence of meteorological conditions and air mass origin on their levels, in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably comprised a quasi-total lockdown in France in Spring, and a lighter one in Autumn. Therefore, a focus is made on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. This dataset could be further used as input for atmospheric models and can be found under https://doi.org/10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al, 2022). [ FROM AUTHOR]

Using Modified Delphi Study to Develop Instrument for ESG Implementation: A Case Study at an Indonesian Higher Education Institution

Most research states that implementing environmental, social, and governance (ESG) has positive impacts. However, fewer studies have discussed ESG implementation in higher education. This study aimed to develop instruments to assess the ESG atmosphere in higher education institutions. A modified Delphi approach was employed. Experts were invited from a private higher education institution in Indonesia. A deductive study, discussion, and two stages of getting consensus from panelists were conducted. The instrument was distinguished into four types for four groups of higher education stakeholders: Students, Staff, Faculty Members, and Community Members. The I-CVIs ranged from 0.80–1.00, while the minimum values of S-CVI/Ave and S-CVI/UA were 0.98 and 0.91, respectively, meaning the content validity was excellent. The final version instrument has been tested and declared valid, reliable, and ready to be used for empirical research for universities to assess their contribution to the Sustainability Development Goals (SDGs). There are also opportunities to conduct further research on the existence of recursive and non-recursive models between factors.

Paleofire Data for Public Health Nursing Wildfire Planning: A Planetary Perspective

Public health Is Increasingly threatened by global warming, land use, and changing wildfire patterns that shape vegetation type, structure, and biodiversity and ultimately affect ecosystem services and our society.1 Uncontrolled large wildfires emit greenhouse gases and aerosols that induce direct and indirect climate feedback through radiative forcing in the atmosphere2 and irreversible changes of natural vegetation, thereby further accelerating climate change and associated fire risks.3 Wildfires are also harmful to human health because they create high pollution concentrations of fine particulate matter that are 2.5 micrometers or smaller (PM2.5) and concentrations of coarse particulate matter that are between 2.5 and 10 micrometers in size. When inhaled, particulate matter significantly increases a myriad of health outcomes, including overall mortality, cardiovascular mortality, and emergency department visits for respiratory morbidity, congestive heart failure, chronic obstructive pulmonary disease, and angina.4,5 Between July and October 2020, high PM2.5 concentrations from massive wildfires surrounding a large regional hospital in the western United States were associated with a 6% increase in COVID-19 cases.6 Risks for developing adverse health effects from wildfire smoke are greatest among people who are living with chronic conditions;who are experiencing intergenerational racial, economic, and housing discrimination;and who are facing social inequities from the COVID-19 pandemic.4The unprecedented recent wildfires in the western United States and their ill effects on human health and society, as well as the multiple other threats to people and places brought about by climate change, draw attention to the increasing urgency of developing new public health approaches and long-term adaptation strategies to support future population health. Observational fire data covering the past few decades give valuable information on current wildfire events.1 However, these data hardly capture long-term trends (i.e., centennial to millennial time scales) of wildfires and associated atmospheric emissions that may help to improve future fire models and thereby provide the base to adapt public health systems.3 To understand long-term trends, natural archives preserve fire history on a wide range ofspatial scales in the past beyond the period of observational fire data;examples include polar and highalpine ice cores;lake, peat, and marine sediment cores.3,8,9 Such paleofire records are based on measurements of the gaseous tracers ammonium and nitrate or particulate matter, such as levoglucosan and black carbon, and charcoal that reflect different components of wildfire-induced atmospheric smoke pollution.8,9 These paleofire records have previously identified complex regional interactions of humans, ecosystems, and climate change.3 Submicron-sized (100-500 nm in diameter) black carbon particles from wildfires and fossil fuel during the industrial era (i.e., the past 250 years) measured in ice cores and lake sediments can be used as a direct tracer for the release of harmful PM2.5 to the atmosphere.8,10 Such paleo black carbon records have been established from both polar and high-alpine glaciers on several continents and are recently developed from lake sediments.10 These found significant changes of fire activity in response to climate and human impact and enhanced pollution levels varying both in time and space. With public health nurses being well positioned to understand population health needs, planetary health, and the health consequences of wildfires, public health nurses can improve upon wildfire adaptation planning and essential public health services by understanding historical perspectives from past fires.9,11,13 Paleofire data provide direct estimates of historical atmospheric emissions from past wildfires and associated harmful concentrations of particulate matter over long distances.

Development of the TOA-Related Models for PM2.5 Prediction Pre- and Post-COVID-19 Outbreak over Yangtze River Delta Region of China

The lockdown and the strict regulation measures implemented by Chinese government due to the outbreak of the COVID-19 pandemic not only decelerated the spread of the virus but also brought a positive effect on the nationwide atmospheric quality. In this study, we extended our previous research on remotely sensed estimation of PM2.5 concentrations in Yangtze River Delta region (i.e., YRD) of China from 2019 to the strict regulation period of 2020 (i.e., 24 Jan, 2020-31 Aug, 2020). Unlike the method using aerosol optical depth (AOD) developed in previous studies, we validated the possibility of moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance (i.e., MODIS TOA) at 21 bands in estimating the PM2.5 concentrations in YRD region. Two random forests (i.e., TOA-sig RF and TOA-all RF) incorporated with different MODIS TOA datasets were developed, and the results showed that the TOA-sig RF model performed better with R2 of 0.81 (RMSE=8.07 μg/m3) than TOA-all RF model with R2 of 0.79 (RMSE=9.13 μg/m3). The monthly averaged PM2.5 exhibited the highest value of 50.81 μg/m3 in YRD region in January 2020 and sharply decreased from February to August 2020. The annual mean PM2.5 concentrations derived by TOA-sig RF model were 47.74, 32.14, and 21.04 μg/m3 in winter, spring, and summer in YRD during the strict regulation period of 2020, respectively, showing much lower values than those in 2019. Our research demonstrated that the PM2.5 concentrations could be effectively estimated by using MODIS TOA reflectance at 21 bands and the random forest.

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time‐series features, along with long‐term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction model by combining generalized autoregressive conditional heteroscedasticity (GARCH) family models, XGBoost algorithm, and long short‐term memory (LSTM) network. The model first takes the GARCH family models parameters of sliding estimation and meteorological factors as the influencing factors of volatility, and then it screens these influencing factors through the extreme gradient boosting (XGBoost) algorithm. Finally, the selected important features are input into the LSTM network to predict the volatility, and the 90% confidence interval of the volatility is calculated. Compared with a variety of single and combined models, the model proposed in this study has an average reduction of 45.404% in the evaluation index of mean squared error. The experimental results show that the model proposed in this study has a good performance and accuracy in predicting the volatility of natural gas load.

Effects of Interface Design and Live Atmosphere on Consumers’ Impulse-Buying Behaviour from the Perspective of Human–Computer Interaction

Since its introduction, live e-commerce has shown rapid growth, especially in regions such as China, where the total market size has exceeded trillions of RMB. However, e-commerce live streaming has also caused widespread consumer impulse-buying behaviour during the development process. Therefore, based on stimulus–organism–response theory, from the perspective of human–computer interaction, this paper develops an impulse-purchase model for live e-commerce consumers, uses partial least squares structural equation modelling to process and analyse 339 valid questionnaires and, finally, validates the proposed hypotheses. The findings show that consumers’ visual appeal, perceived arousal and engagement play a mediating role in the relations among interface design, live atmosphere and impulse purchase. To promote the sustainability of a live-streaming economy, live-streaming platforms need to design attractive live-streaming interfaces, build a pleasant live-streaming atmosphere and enhance consumers’ positive emotions, while preventing their irrational purchasing behaviour. An in-depth analysis of the formation mechanism of this behaviour can help in alleviating the limitations of the lack of rich research results and a single perspective in this field. In addition, it can help stakeholders promote the sustainability of e-commerce live streaming in practice.

Modeling Surface Air Pollution with Reduced Emissions during the COVID-19 Pandemic Using CHIMERE and COSMO-ART Chemical Transport Models

The results of numerical modeling of air pollution using CHIMERE and COSMO-ART chemical transport models are presented. The modeling was performed according to the scenarios of the 50–60% reduction of emissions from anthropogenic sources in the Moscow region during the period of March–July 2020. Scenario calculations of pollutant concentrations were compared with baseline simulations using regionally adapted inventory of anthropogenic pollutant emissions to the atmosphere. The most significant decrease in the concentrations of NO2 and CO was reproduced by the models when emissions from two sectoral sources (vehicles and nonindustrial plants) were reduced. The PM10 drop was mostly influenced by the reduction of emissions from industrial combustion. With the total reduction of emissions from anthropogenic sources as compared to the baseline calculations, the pollutant concentration decreased by 44–54% for NO2, by 38–44% for CO, and by 26–39% for PM10. This generally coincides with the quantitative estimates of the pollution level drop obtained by other authors. The greatest effect of reducing pollutant emissions into the atmosphere was found during the episodes of adverse weather conditions for air purification, when the simulated and observed pollution level increases by 3–5 times as compared to the conditions of intense pollutant dispersion.

Progress and Challenges in Modeling Dynamics–Microphysics Interactions: From the Pi Chamber to Monsoon Convection

The CAIPEEX (Cloud Aerosol Interaction and Precipitation Enhancement Experiment) monsoon convective clouds case was designed to explore the impacts of environmental and cloud condensation nuclei (CCN) conditions on monsoon convection. Pi chamber warm cloud case The scientific objectives are 1) to demonstrate the model capability of representing the detailed microphysical processes happening in the cloud chamber and how different models behave in different aerosol injection rates, 2) to reveal the model uncertainties and limitations in the existing modeling tools, and 3) to provide guidance and recommendations for future work to improve cloud chamber simulations and model–laboratory comparisons. The comparison was performed among a diverse set of model categories, including four types of LES models (Dziekan et al. 2019;Shima et al. 2009, 2020;Niedermeier et al. 2020;Khairoutdinov and Randall 2003) performed by the University of Warsaw, University of Hyogo, Leibniz Institute for Tropospheric Research, and Brookhaven National Laboratory;two types of direct numerical simulation (DNS) models (Chen et al. 2021;Richter et al. 2021) by the National Center for Atmospheric Research (NCAR) and the University of Notre Dame;and the Linear Eddy Model (LEM;Su et al. 1998) by the University of Utah. Interestingly, however, the amount of updraft tilting was sensitive not only to the vertical wind shear used in the model, but also to the method of cloud initiation, i.e., forcing using warm bubbles or surface heat fluxes.

A Succession of Cloud, Precipitation, Aerosol, and Air Quality Field Experiments in the Coastal Urban Environment

Within this context, fundamental questions regarding the life cycle of convective clouds, aerosols, and pollutants have brought together a diverse, integrated, and interagency collaboration of scientists to collect and analyze measurements, in the Houston, Texas, area, from the summer of 2021 through the summer of 2022, with subsequent modeling studies to address these important research objectives. The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility and Atmospheric System Research (ASR) Program, the National Science Foundation’s (NSF’s) Physical and Dynamic Meteorology Program, the National Aeronautics and Space Administration’s (NASA’s) Tropospheric Composition Research and Health and Air Quality Applied Sciences Programs and the Texas Commission on Environmental Quality (TCEQ) are collaborating on a joint set of field campaigns to study the interactions of cloud, aerosol, and pollutants within the coastal, urban environment. Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). On the ground, multiple fixed and mobile radar systems (Fig. 1a) will be used to track convective cells and perform multi-Doppler analysis for the derivation of velocities within the convective systems over the course of their life cycle.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (N1 -https://media.proquest.com/media/hms/PFT/1/TPT6N?_a=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%3D%3D&_s=Os82DX%2BaBlhnLe1wnAEkKTgdQ4M%3D ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.

Deep Learning for Predicting Winter Temperature in North China

It is difficult to improve the seasonal prediction skill of winter temperature over North China, owing to the complex dynamics of East Asian winter and the relatively low prediction skill level of current climate models. Deep learning (DL) may be an informative and promising tool to enhance seasonal prediction, particularly in regions where the underlying mechanisms are not clear. Here, using a DL model based on the Convolutional Neural Network (CNN), we have found that the prediction skill for North China winter temperature (NCWT) can be extended up to five months by considering the remote impact of the Northeast Pacific sea-surface temperature (SST) on North China. Based on historical simulations of winter temperatures in North China, we selected six CMIP5 models with relatively small deviations for training the CNN, and the period chosen for training was 1852–1991. The N1 -https://media.proquest.com/media/hms/PFT/1/Ruo5N?_a=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%3D%3D&_s=2fC0CTd0WocPaF%2FXuQegxUXRgWY%3D ERA5 data during 1995–2017 were utilized to evaluate the performance of the CNN. Our CNN shows the best performance in a recent 10-year period (2008–2017), showing a significantly improved level of NCWT prediction skill with a correlation skill of 0.65 at a 5-month lead time, which is much better than the forecast skill of the state-of-the-art dynamic seasonal prediction system. Heat map analysis was used to explore the possible physical mechanisms associated with the NCWT anomaly from the perspective of the CNN;the results showed that the SST over the Northeast Pacific is highly relevant to NCWT prediction. The Northeast Pacific warming in the boreal summer is related to the development of the El Niño event in the coming winter, which may induce NCWT anomalies by atmospheric teleconnection. Climate model experiments support the role of Northeast Pacific warming in the boreal summer on NCWT. The improved capability for prediction from using the CNN may help to establish the energy policy for the coming winter and reduce the economic losses from extremely cold in North China.