Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts

Long-term investigation of methane and carbon dioxide emissions in two Italian landfills

Landfills play a key role as greenhouse gas (GHGs) emitters, and urgently need assessment and management plans development to swiftly reduce their climate impact. In this context, accurate emission measurements from landfills under different climate and management would reduce the uncertainty in emission accounting. In this study, more than one year of long-term high-frequency data of CO2 and CH4 fluxes were collected in two Italian landfills (Giugliano and Case Passerini) with contrasting management (gas recovery VS no management) using eddy covariance (EC), with the aim to i) investigate the relation between climate drivers and CO2 and CH4 fluxes at different time intervals and ii) to assess the overall GHG balances including the biogas extraction and energy recovery components. Results indicated a higher net atmospheric CO2 source (5.7 ± 5.3 g m2 d-1) at Giugliano compared to Case Passerini (2.4 ± 4.9 g m2 d-1) as well as one order of magnitude higher atmospheric CH4 fluxes (6.0 ± 5.7 g m2 d-1 and 0.7 ± 0.6 g m2 d-1 respectively). Statistical analysis highlighted that fluxes were mainly driven by thermal variables, followed by water availability, with their relative importance changing according to the time-interval considered. The rate of change in barometric pressure (dP/dt) influenced CH4 patterns and magnitude in the classes ranging from -1.25 to +1.25 Pa h-1, with reduction when dP/dt > 0 and increase when dP/dt < 0, whilst a clear pattern was not observed when all dP/dt classes were analyzed. When including management, the total atmospheric GHG balance computed for the two landfills of Giugliano and Case Passerini was 174 g m2 d-1 and 79 g m2 d-1 respectively, of which 168 g m2 d-1 and 20 g m2 d-1 constituted by CH4 fluxes.

COVID lockdown significantly impacted microplastic bulk atmospheric deposition rates

Here, microplastic atmospheric deposition data collected at an urban site during the French national lockdown of spring 2020 is compared to deposition data from the same site in a period of normal activity. Bulk atmospheric deposition was collected on the vegetated roof of a suburban campus from the Greater Paris and analysed for microplastics using a micro-FTIR imaging methodology. Significantly lower deposition rates were measured overall during the lockdown period (median 5.4 MP m-2.d-1) than in a period of normal activity in spring 2021 (median of 29.2 MP m-2.d-1). This difference is however not observed for the smallest microplastic size class. The dominant polymers identified were PP, followed by PE and PS. Precipitation alone could not explain the differences between the two campaigns, and it is suggested that the temporary drop in human activity during lockdown is the primary cause of the reduced deposition rates. This study provides novel insight on the immediate impact of human activities on atmospheric microplastics, thus enhancing the global understanding on this topic.

The pandemic effect on GHG emission variation at the sub-national level and translation into policy opportunities

Greenhouse gas (GHG) emissions inventories are commonly compiled at country level to monitor national progress towards nationally or internationally agreed targets. While they can support national climate change mitigation strategies, accounting for the intra-national heterogeneity of a country can draw different conclusions directly linked to the socio-economic and environmental sub-national context. This means that more refined and accurate policies and mitigation strategies can be designed when supported by GHG inventories at sub-national scale. The differences between sub-national territorial emissive behavior can be revealed by subjecting different territories to the same stress factors. A complete GHG emissions inventory, based on the Intergovernmental Panel on Climate Change (IPCC) Guidelines, is compiled for three diverse administrative territories, in terms of scale, socio-economic contexts, and environmental conditions. By selecting three diverse sub-national contexts belonging to the same national territory - Italy - the analysis provides highly detailed information on the emissive status and behavior and delivers insights that national inventories fail to provide. The COVID-19 pandemic is considered as a stress factor; therefore, the reference years are 2019 and 2020 during which GHG emissions are detected. The study will test the capacity of sub-national GHG emission inventories, compiled by scaling the IPCC methodology to the sub-national level, to detect such differences through the lens of the pandemic. This allows obtaining detailed information and linking the pandemic effect to the GHG emissions of particular activities, which can inspire effective sub-national context-specific mitigation actions. Furthermore, we show that environmental and economic metrics are not as strictly coupled as they would appear at national level.

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

A high-resolution monitoring approach of urban CO2 fluxes. Part 2 - surface flux optimisation using eddy covariance observations

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Can minimizing risk exposures help in inhibiting carbon footprints? The environmental repercussions of international trade and clean energy

Since bettering environmental conditions has acquired significant interest globally, discovering factors that may facilitate the establishment of environmental sustainability is currently of foremost importance. Hence, this study considers a sample of 33 members of the Organization for Economic Cooperation and Development and checks whether reducing exposure to different forms of country risks, in the presence of international trade and clean energy consumption, can reduce their respective carbon footprint levels. Utilizing annual data from 2000 to 2018 and employing methods that handle problems related to dependence across cross-sectional units and heterogeneity of slope coefficients, the findings endorse that (a) reducing financial and political risks abate carbon footprints, (b) economic risk exposure does not influence carbon footprints, (c) international trade exerts carbon footprint-boosting effects, and (d) undergoing unclean to clean energy transition curbs carbon footprints. Accordingly, the concerned governments should these findings into account while conceptualizing green environmental policies in the future.

Satellite reveals a steep decline in China's CO2 emissions in early 2022

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

The impact of financial tools in environmental degradation management: the relationship between Co2 emission and ESG funds

This study aims to determine whether ESG funds can be used as an effective tool for environmental sustainability. ESG funds, which first appeared in the 2000s and were exported by environmentally friendly companies, are among the most effective tools for increasing firm value and managing environmental degradation. The causality relationship between the ESG funds, one of the environmentally friendly investment instruments, and the CO2 emission values, which are used as an environmental degradation criterion, was investigated in this study. The study used 209 daily data sets from July 31, 2020, to May 28, 2021. The symmetric developed by Hacker and Hatemi-J (Appl Econ 38:1489-1500, 2006), the asymmetric developed by Hatemi-J (Empir Econ 43:447-456, 2012), and time-varying asymmetric causality tests were used as models. According to the study results, while there is no symmetric causality between CO2 emissions and ESG funds, there is causality between CO2 emissions and ESG funds prices for negative shocks and between CO2 emissions and ESG funds trade volume for positive shocks. The results of a time-varying asymmetric causality test also support that this causality relationship varies by period. As a result, ESG funds can be used as a strategic financial tool to improve environmental quality during the COVID-19 period; however, this may vary for different sub-sample periods.

Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions

Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of “CO2 emissions due to the fuel types” on “percentage of deaths in total cases” attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as “percentage of deaths in total cases attributed to the COVID-19 pandemic” calculated as “(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)*100.” The explanatory variables are taken as “production-based emissions of CO2 from different fuel types,” measured in tonnes per person, which are “coal, cement, flaring, gas, and oil.” As a result of this study, according to the goodness-of-fit test statistics, “GLMM approach with gamma distribution” called “gamma mixed regression model” is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types “cement, coal, flaring, gas, and oil” per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively.

Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI =  - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI =  - 0.43- - 0.08), and residential places (- 1.02; 95% CI =  - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI =  - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.

COVID-19 pandemic underscores role of green space in urban carbon dynamics

For Detroit Michigan the arrival of COVID-19 led to intensive measures to prevent further spread of the virus resulting in consequent changes in traffic and energy use. We take advantage of these different emission scenarios to explore CO₂ dynamics in a postindustrial city with a declining population and increasing green space. We present atmospheric CO₂ concentration and net urban ecosystem exchange of CO₂ (NUE) from a typical eddy covariance system and canopy greenness from a field camera on the Wayne State University campus in midtown Detroit. We categorized our study period (January 18, 2020-July 31, 2020) into three subperiods associated with the state-wide shelter-in-place order. Our results support that the city was a net carbon source throughout the period, particularly during the shelter-in-place period, although reduced traffic lowered CO₂ concentrations and NUE. However, during the post-order period when traffic was highest, atmospheric CO₂ concentrations and NUE were lowest, suggesting that the greening of urban vegetation may have greater carbon mitigation potential than lowering anthropogenic carbon emissions through traffic reductions.

Role of the e-exhibition industry in the green growth of businesses and recovery

In this paper, a survey and two multi-attribute decision-making (MADM) models have been employed to explore critical success factors of e-exhibition in 30 Chinese provinces that is divided into 8 different regions. The research findings showed that in China, the most important success factors of e-exhibition to have green economic recovery are the presence of International collaboration (0.592), green culture (0.490), and visitor’s attitude (0.439). Furthermore, “Beijing and Tianjin” is the most ideal region to promote e-exhibition in China. South Coast region ranked in second place as the most appropriate region for e-exhibition. The least ideal region of China for e-exhibition is the Southwest region that is less developed compared to other regions of China. The major practical policies are the enhancement of international cooperation to hold an e-exhibition, use of electronic exhibition capacities (synchronous and asynchronous) and creating social sustainability awareness through the media and social network.

How Does Health Uncertainty Impact Greenhouse Gas Emissions in European Union Economies? A Blessing in Disguise

The global outbreak of COVID-19 caused serious threats to public health and economic growth all around the world, but on the other hand, the betterment of the environment took place. How pandemics' health uncertainty will affect environmental quality is a crucial matter to address. The paper investigates the asymmetric association between pandemics-related health uncertainty and greenhouse gas emissions (GHG) in the top emitter European Union economies (Italy, Germany, France, Poland, Netherlands, Spain, Czech Republic, Belgium, Romania, and Greece). Employing data from 1996 to 2019, a unique approach called 'Quantile-on-Quantile', is adopted to evaluate the influence of various quantiles of the health uncertainty on GHG emissions. According to estimates, health uncertainty enhances environmental quality by minimizing GHG in most of our chosen nations at certain quantiles of data, which makes pandemics a blessing in disguise for environmental quality. Additionally, the estimations indicate that the grades of asymmetry between our variables varies by locality, accentuating the requisite for authorities to give specific consideration while executing health uncertainty and environmental quality policies.

Short run "rebound effect" of COVID on the transport carbon footprint

The COVID-19 pandemic completely transformed the mobility of cities. The restrictions on movement led to "empty cities" throughout the world, with some environmental effects in terms of clean air and the reduction of CO₂ emissions. This research considers how COVID-19 mobility restrictions have affected the carbon footprint of four medium-sized Chilean cities (Coronel, Temuco, Valdivia, and Osorno) that have environmental problems and are highly dependent on motorized systems. The study uses data from 2400 household surveys at three distinct times: pre-pandemic - T0 (winter 2019), the time of implementation of restrictive mobility policies to contain the pandemic - T1 (winter 2020), and six months later when those restrictions were gradually lifted - T2 (summer 2021). The analysis suggests that CO₂ emissions actually went up, declining in the winter 2020, but then increasing with the greater use of cars in the summer 2021 due to the temporary effects of commuting to work, ultimately reaching levels higher than the pre-pandemic values, known as the "rebound effect."

Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO₂ and PM₁₀ at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017-2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO₂ (R² = 0.72) than PM₁₀ concentrations (R² = 0.35). The average decrease of NO₂ was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM₁₀ concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.

Significant reduction of ultrafine particle emission fluxes to the urban atmosphere during the COVID-19 lockdown

The worldwide restrictions of social contacts that were implemented in spring 2020 to slow down infection rates of the SARS-CoV-2 virus resulted in significant modifications in mobility behaviour of urban residents. We used three-year eddy covariance measurements of size-resolved particle number fluxes from an urban site in Berlin to estimate the effects of reduced traffic intensity on particle fluxes. Similar observations of urban surface-atmosphere exchange of size-resolved particles that focus on COVID-19 lockdown-related effects are not available, yet. Although the site remained a net emission source for ultrafine particles (UFP, D_p < 100 nm), the median upward flux of ultrafine particles (F_UFP) decreased from 8.78 × 10⁷ m^-2 s^-1 in the reference period to 5.44 × 10⁷ m^-2 s^-1 during the lockdown. This was equivalent to a relative reduction of -38 % for median F_UFP, which was similar to -35 % decrease of road traffic intensity in the flux source area during that period. The size-resolved analysis demonstrated that, on average, net deposition of UFP occurred only during night when particle emission source strength by traffic was at its minimum, whereas accumulation mode particles (100 nm < D_p < 200 nm) showed net deposition also during daytime. The results indicate the benefits of traffic reductions as a mitigation strategy to reduce UFP emissions to the urban atmosphere.