Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

Effects of COVID-19 on passenger shipping activities and emissions: empirical analysis of passenger ships in Danish waters

This paper focuses on the analysis of the COVID-19 effects on passenger shipping in Danish waters as an example and aims to analyse the differences in passenger vessel activities and emissions before and after the COVID-19 outbreak. Two sets of Automatic Identification System (AIS) data for the passenger ships sailing in Danish waters associated with the whole year respectively for 2020 and 2019 are used for a comprehensive evaluation of the passenger shipping activities in the region by means of the analysis of variance and bottom-up emission models. A comparison of those results based on the two datasets shows that the COVID-19 pandemic has a major impact on cruise ships, with a significant reduction in the number of ships, average speed, and average draught. In contrast, the pandemic has a smaller impact on ferry-pax only and ferry-ro pax vessels. The effects can also be seen from the fact that, after the COVID-19 outbreak, SOx emissions from cruise ships, ferry-pax only and ferry-ro pax vessels were reduced by 50.71%, 0.51% and 0.82%, respectively. This investigation provides an important reference for policy makers in the marine environment sector.

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.

Impact of Anthropogenic Emission Reduction during COVID-19 on Air Quality in Nanjing, China

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China

PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

Complex of Ceftriaxone with Mg(II): Synthesis, Structure, Spectral and Antibacterial Properties

Аннотация. Получен и охарактеризован магниевый комплекс цефтриаксона методами атомно-эмиссионного и элементного анализов, ТГА, ИК- и КР-спектроскопии, РФА и расчетов теории функционала плотности. Цефтриаксон координируется к иону магния через кислород триазинового цикла в шестом положении, азот аминогруппы тиазольного цикла и атомы кислорода карбоксильной и лактамной групп. Динатриевая соль цефтриаксона и комплекс магния были исследованы на антибактериальную активность в отношении Staphylococcus aureus, Escherichia coli и Pseudomonas aeruginosa.Alternate abstract:Magnesium complex of ceftriaxone was obtained and characterized by atomic-emission and elemental analysis, TGA, FTIR and Raman spectroscopy, X-ray diffraction and density functional theory calculations. Ceftriaxone was coordinated to the magnesium ion by the oxygen of the triazine cycle in the 6th position, the nitrogen of the amine group of the thiazole ring, and oxygen atoms of the lactam carbonyl and carboxylate groups. The disodium salt of ceftriaxone and magnesium complex were screened for antibacterial activity against Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa.

Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Incidence of incidental positron emission tomography and computed tomography signs of COVID-19 in asymptomatic cancer patients in Egypt

Contexte : La COVID-19 a été signalée pour la première fois en Égypte le 14 février 2020 et demeure une menace majeure pour la santé publique. Objectifs : Nous avons étudié l'incidence des signes fortuits de COVID-19 détectés au moyen de la tomographie par émission de positons/de la tomodensitométrie (TEP/TDM) chez des patients asymptomatiques atteints de cancer. Puis, nous avons comparé cette incidence au nombre de cas de COVID-19 notifiés pendant la même période. Méthodes : Nous avons inclus tous les patients atteints de cancer qui ont subi une TEP/TDM au Misr Radiology Center, au Caire, entre le 2 mai et le 7 août 2020. Résultats : Au total, 479 patients ont subi une TEP/TDM principalement à des fins de suivi, et 66 (13,78 %) d'entre eux ont présenté des signes radiologiques de COVID-19, avec un pic d'incidence au cours des semaines sept et huit de l'étude. Cela a coïncidé et était fortement corrélé avec le pic d'incidence de la COVID-19 en Égypte (test du coefficient de corrélation de Pearson = 0,943). Conclusion : L'incidence des signes fortuits de COVID-19 détectés par TEP/TDM était conforme à l'incidence officiellement notifiée de la COVID-19 en Égypte entre le 2 mai et le 7 août 2020. Ces résultats pourraient être utiles pour mettre en œuvre et ajuster les mesures sociales et de santé publique durant la pandémie de COVID-19.Alternate :Background: COVID-19 was first reported in Egypt on 14 February 2020 and continues to be a major threat to public health. Aims: We studied the incidence of incidental positron emission tomography/computed tomography (PET/CT) signs of COVID-19 in asymptomatic cancer patients and compared this with the number of reported COVID-19 cases during the same period. Methods: We included all cancer patients who underwent PET/CT at Misr Radiology Center, Cairo, between 2 May and 7 August 2020. Results: There were 479 patients who underwent PET/CT primarily for follow-up, and 66 (13.78%) of them showed radiological signs of COVID-19, with the peak incidence in weeks 7-8 of the study. This coincided and strongly correlated with the peak incidence of COVID-19 in Egypt (Pearson's correlation coefficient test = 0.943). Conclusion: The incidence of incidental PET/CT signs of COVID-19 was in accordance with the officially reported incidence of COVID-19 in Egypt between 2 May and 7 August 2020. These results could be helpful for implementing and adjusting public health and social measures during the COVID-19 pandemic.

Impact of National Movement Control Orders toward Ground Level Ozone Concentrations in Shah Alam

Malaysia is currently facing the COVID-19 pandemic which has claimed hundreds of innocent lives. Because of the health problems impose by the pandemic, the government has ordered to implement the movement control order (MCO) starting March 18, 2020. With this movement control order in place, people are not allowed to leave the house. Therefore, movement on the road can also be reduced. This study was conducted to investigate the emission concentrations of ground-level ozone into the air during MCOs in an urbanized area of Shah Alam. This secondary data of ground-level ozone (O3) was acquired from the Department of Environment in 2020 and interpreted using the methods of box and whisker plot, time series analysis, and diurnal variation plot. The results found that the concentrations of air pollutants in each type of MCOs implemented were different in terms of trends. During the early implementations of MCOs, results showed that there was a slight decrement in O3 concentrations and as MCOs periods continued, there higher decrements in O3 concentrations were observed. During the MCOs period, non-exceedance episodes were recorded which show the level of ground-level ozone was significantly improved result of the implementation of MCOs. Results also suggested there is a shift in the peak concentration time as the plot showed peak concentrations were reached between 4 to 6 p.m. which is quite later as normally peaks O3 concentrations were reported normally reached from 12 noon to 2 p.m. [13]

Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins

We use satellite methane observations from the Tropospheric Monitoring Instrument (TROPOMI), for May 2018 to February 2020, to quantify methane emissions from individual oil and natural gas (O/G) basins in the US and Canada using a high-resolution (∼25 km) atmospheric inverse analysis. Our satellite-derived emission estimates show good consistency with in situ field measurements (R=0.96) in 14 O/G basins distributed across the US and Canada. Aggregating our results to the national scale, we obtain O/G-related methane emission estimates of12.6±2.1 Tg a-1 for the US and 2.2±0.6 Tg a-1 for Canada, 80 % and 40 %, respectively, higher than the national inventories reported to the United Nations. About 70 % of the discrepancy in the US Environmental Protection Agency (EPA) inventory can be attributed to five O/G basins, the Permian, Haynesville, Anadarko, Eagle Ford, and Barnett basins, which in total account for 40 % of US emissions. We show more generally that our TROPOMI inversion framework can quantify methane emissions exceeding 0.2–0.5 Tg a-1 from individual O/G basins, thus providing an effective tool for monitoring methane emissions from large O/G basins globally.

Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.

Differential responses of soil nitrogen‐oxide emissions to organic substitution for synthetic fertilizer and biochar amendment in a subtropical tea plantation

Tropical and subtropical acidic soils have been well documented as hotspots of global soil nitrogen (N) oxide (i.e., nitrous oxide (N2O) and nitric oxide (NO) emissions). While the effectiveness of possible mitigation options has been extensively examined in croplands, little is known about their effectiveness in reducing N‐oxide emissions from acidic soils of rapidly expanding tea plantations in China. Here, we conducted a 2‐year field experiment to investigate how organic substitution for synthetic fertilizer and biochar amendment affect soil N‐oxide emissions from a subtropical tea plantation. Across the 2‐year measurement period, full organic substitution for synthetic fertilizer significantly increased N2O emissions by an average of 17% while had a lower NO emission compared to synthetic fertilizer alone. Our global meta‐analysis further revealed that full or partial organic fertilizer substitution resulted in a 29% (95% confidence interval: 5%–60%) increase of N2O emissions from acidic soils. In contrast, irrespective of fertilizer type, biochar amendment significantly reduced N2O emissions by 14% in the first but not second experimental year, suggesting a transient effect. The trade‐off effect of full organic substitution on N2O and NO emissions may be attributed to the favorable conditions for N2O production due to the stimulated activity of nitrifiers and denitrifiers. The suppression of N2O emission following biochar amendment was probably due to promoted further reduction of N2O to dinitrogen. The fertilizer‐induced emission factor (EF) of N2O (2.1%) in the tea plantation was greater than the current IPCC default value, but the EF of NO (0.8%) was comparable to the global estimate. Taken together, while biochar amendment could have mitigation potential, cautions are needed when applying organic substitution for synthetic fertilizer as mitigation options for acidic soils as hotspots of N‐oxide emissions.

Carbon Emission Measurement of Urban Green Passenger Transport: A Case Study of Qingdao

Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant.

Measuring Pollution Control and Environmental Sustainable Development in China Based on Parallel DEA Method

The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO2 and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.

PM10 variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions

To control the spread of COVID-19, exceptional restrictive measures were taken in March 2020 that imposed a radical change on the lifestyle of millions of citizens around the world, albeit for a short period. The national lockdown, which lasted from 10 March to 18 May 2020 in Italy, was a unique opportunity to observe the variation in air quality in urban environments under conditions comprising almost total traffic restriction and a strong reduction in work activities. In this paper, the data from 17 urban monitoring sites in Tuscany are presented, and the PM and NO2 concentrations in the 2 months before the start of the lockdown and the 2 months after lockdown are compared with the corresponding months of the previous 3 years. The results show that the total loads of PM2.5 and PM10 (particulate matter with an aerodynamic diameter smaller than 2.5 and 10 µm, respectively) decreased, but they did not exhibit significant changes compared to previous years, whereas NO2 underwent a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured using thermal–optical techniques, ion chromatography, and particle-induced X-ray emission analysis, and the application of multivariate positive matrix factorization analysis also allowed for PM10 source identification and apportionment. Using these analyses, it was possible to explain the low sensitivity of PM10 to the lockdown effects as being due to different, sometimes inverse, behaviors of the different sources that contribute to PM. The results clearly indicate a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.

Effect of Al2O3 Nanoparticles on Performance and Emission Characteristics of Diesel Engine Fuelled with Diesel–Neem Biodiesel Blends

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines.

The Carbon Footprint of Fruit Storage: A Case Study of the Energy and Emission Intensity of Cold Stores

Despite their importance in all transportation chains, logistical sites—and in particular refrigerated facilities—are the weakest link in current emissions literature. This is largely due to a lack of quantitative research that focuses on these facilities. This article is the first of its kind to assess the emissions of eight refrigerated facilities that handle and store fresh fruit. In 2020, the analyzed facilities moved a total of 646,572 pallets of fresh fruit and emitted 32,225 t of CO2e. Five of the largest facilities were responsible for handling 18.83% of the total fresh fruit exported from South Africa during 2020. The results revealed that storing and handling a pallet of fruit in a large-scale commercial cold store requires 7.62 kWh of electricity per day. Storing and handling fresh fruit is carbon intensive since each pallet stored translates to 7.52 kg CO2e d−1. However, other factors such as the seasonality and volume of fruit handled, facility characteristics and the availability of solar electricity systems, among others, all have a significant impact on the emissions value of the facility and on the emission intensity per pallet moved through the facility.

Spatiotemporal Variation and Driving Factors of Embodied Carbon in China-G7 Trade

China and G7 countries contribute 70% global GDP and 55% global carbon emissions. The carbon leakage between China and G7 is a crucial issue in achieving the synergetic emission abatement globally. The motivation of this study is to evaluate the embodied carbon transfer between China and G7 in the trade between 2000 and 2014, and investigate the driving factors that impact the embodied carbon trend. A multiregional input–output (MRIO) model based on the WIOD database is constructed, and a structural decomposition analysis (SDA) is employed. The results indicate that China plays the role of net exporter of embodied carbon in trade with G7, which mainly flows to the US (5825.67 Mt), Japan (3170.36 Mt) and Germany (1409.93 Mt). However, China’s embodied carbon exports to the G7 show an inverted U-shaped trend with a turning point after financial crisis, while the G7’s embodied carbon exports to China continue to rise. The conclusion is that to achieve the climate goal of carbon neutrality, it is not enough to rely solely on the low-carbon transition on the production side, the demand side should also be adjusted.

Impact of COVID-19 Pandemic Preventing Measures and Meteorological Conditions on the Atmospheric Air Composition in Moscow in 2020

Changes in the atmospheric composition during different periods of 2020 in Moscow which were associated with the COVID-19 pandemic preventing measures as well as corresponding pollutant emission reduction, are investigated. Surface concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), aerosol fraction (PM10), and meteorological parameters during different periods of 2020 were compared with similar data for the previous five years. The analysis of ground-based measurements, as well as of high-resolution satellite distributions of CO and NO2 indicated that the concentration of major pollutants and its spatial distribution in the Moscow region were significantly affected by both restrictive measures and abnormal meteorological conditions in 2020.