ABSTRACT
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.
ABSTRACT
Fluxes of nitrogen oxides (NOx=NO+NO2) and carbon dioxide (CO2) were measured using eddy covariance at the British Telecommunications (BT) Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 73 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled, identifying that transport NOx emissions had reduced by >73 % since 2017. This was attributed in part to the success of air quality policy in central London but crucially due to the substantial reduction in congestion that resulted from pandemic-reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which, until now, has been primarily focused on the emissions from diesel exhausts.
ABSTRACT
In the summer of 2020, the AVIRIS-NG airborne imaging spectrometer surveyed California's Southern San Joaquin Valley and the South Bay (Los Angeles County) to identify anthropogenic methane (CH4) point source plumes, estimate emission rates, and attribute sources to both facilities and emission sectors. These flights were designed to revisit regions previously surveyed by the 2016-2017 California Methane Survey and to assess the socioeconomic responses of COVID-19 on emissions across multiple sectors. For regions flown by both the California Methane Survey and the California COVID campaigns, total CH4 point source emissions from the energy and oil & natural gas sectors were 34.8% lower during the summer 2020 flights, however, emission trends varied across sector. For the energy sector, there was a 28.2% decrease driven by reductions in refinery emissions consistent with a drop in production, which was offset in part with increases from powerplants. For the oil & natural gas sector, CH4 emissions declined 34.2% and significant variability was observed at the oilfield scale. Emissions declined for all but the Buena Vista and Cymric fields with an observed positive relationship between production and emissions. In addition to characterizing the short-term impact of COVID-19 on CH4 emissions, this study demonstrates the broader potential of remote sensing with sufficient sensitivity, spatial resolution, and spatio-temporal completeness to quantify changes in CH4 emissions at the scale of key sectors and facilities. © 2023 The Author(s). Published by IOP Publishing Ltd.
ABSTRACT
BACKGROUND: The arrival of the Delta variant of SARS-CoV-2 was associated with increased transmissibility and illness of greater severity. Reports of nosocomial outbreaks of Delta variant COVID-19 in acute care hospitals have been described but control measures varied widely. AIM: Epidemiological investigation of a linked two-ward COVID-19 Delta variant outbreak was conducted to elucidate its source, risk factors, and control measures. METHODS: Investigations included epidemiologic analysis, detailed case review serial SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) testing of patients and healthcare workers (HCWs), viral culture, environmental swabbing, HCW-unaware personal protective equipment (PPE) audits, ventilation assessments, and the use of whole genome sequencing (WGS). FINDINGS: This linked two-ward outbreak resulted in 17 patient and 12 HCW cases, despite an 83% vaccination rate. In this setting, suboptimal adherence and compliance to PPE protocols, suboptimal hand hygiene, multi-bedded rooms, and a contaminated vital signs cart with potential fomite or spread via the hands of HCWs were identified as significant risk factors for nosocomial COVID-19 infection. Sudden onset of symptoms, within 72 h, was observed in 79% of all Ward 2 patients, and 93% of all cases (patients and HCWs) on Ward 2 occurred within one incubation period, consistent with a point-source outbreak. RT-PCR assays showed low cycle threshold (CT) values, indicating high viral load from environmental swabs including the vital signs cart. WGS results with ≤3 SNP differences between specimens were observed. CONCLUSION: Outbreaks on both wards settled rapidly, within 3 weeks, using a `back-to-basics' approach without extraordinary measures or changes to standard PPE requirements. Strict adherence to recommended PPE, hand hygiene, education, co-operation from HCWs, including testing and interviews, and additional measures such as limiting movement of patients and staff temporarily were all deemed to have contributed to prompt resolution of the outbreak.
ABSTRACT
Superspreading events and overdispersion are hallmarks of the COVID-19 pandemic. However, the specific roles and influence of established viral and physical factors related to the mechanisms of transmission, on overdispersion, remain unresolved. We, therefore, conducted mechanistic modeling of SARS-CoV-2 point-source transmission by infectious aerosols using real-world occupancy data from more than 100 000 social contact settings in ten US metropolises. We found that 80% of secondary infections are predicted to arise from approximately 4% of index cases, which show up as a stretched tail in the probability density function of secondary infections per infectious case. Individual-level variability in viral load emerges as the dominant driver of overdispersion, followed by occupancy. We then derived an analytical function, which replicates the simulated overdispersion, and with which we demonstrate the effectiveness of potential mitigation strategies. Our analysis, connecting the mechanistic understanding of SARS-CoV-2 transmission by aerosols with observed large-scale epidemiological characteristics of COVID-19 outbreaks, adds an important dimension to the mounting body of evidence with regard to airborne transmission of SARS-CoV-2 and thereby emerges as a powerful tool toward assessing the probability of outbreaks and the potential impact of mitigation strategies on large scale disease dynamics. © 2022 Author(s).
ABSTRACT
Eco-efficiency of arable land utilization (EALU) emphasizes efficient coordination between land use systems and ecosystems. It is therefore of great significance for agricultural sustainability based on the systematic assessment of EALU. This study took carbon emissions and non-point source pollution resulting from arable land utilization into the measurement system of EALU, and a super-SBM model, kernel density estimation and Tobit regression model were used to analyze regional differences and influencing factors of EALU for 31 provinces in China from 2000 to 2019. The results showed that there was an upward trend in EALU in China from 0.4393 in 2000 to 0.8929 in 2019, with an average annual growth rate of 4.01%. At the regional level, the EALU of three categories of grain functional areas generally maintains an increasing trend, with the highest average value of EALU in main grain marketing areas (MGMAs), followed by grain producing and marketing balance areas (GPMBAs) and main grain producing areas (MGPAs). There are obvious differences in EALU among provinces, and the number of provinces with high eco-efficiency has increased significantly, showing a spatial distribution pattern of “block” clustering. In terms of dynamic evolution, kernel density curves reflect the evolution of EALU in China and grain functional areas with different degrees of polarization characteristics. The results of Tobit regression show that natural conditions, financial support for agriculture, science and technology inputs, level of industrialization, agricultural mechanization, and the living standards of farmers are significant factors resulting in regional disparities of EALU. Therefore, this study proposes the implementation of differentiated arable land use/agricultural management strategies to improve the sustainable utilization of arable land.
ABSTRACT
This study utilized MIKE 11 to quantify the spatio-temporal dynamics of water quality parameters (Biochemical Oxygen Demand (BOD5), Dissolved Oxygen (DO) and temperature) in the Long Xuyen Quadrangle area of the Vietnamese Mekong Delta. Calibrated for the year of 2019 and validated for the year of 2020, the developed model showed a significant agreement between the observed and simulated values of water quality parameters. Locations near to cage culture areas exhibited higher BOD5 values than sites close to pond/lagoon culture areas due to the effects of numerous point sources of pollution, including upstream wastewater and out-fluxes from residential and tourism activities in the surrounding areas, all of which had a direct impact on the quality of the surface water used for aquaculture. Moreover, as aquacultural effluents have intensified and dispersed over time, water quality in the surrounding water bodies has degraded. The findings suggest that the effective planning, assessment and management of rapidly expanding aquaculture sites should be improved, including more rigorous water quality monitoring, to ensure the long-term sustainable expansion and development of the aquacultural sector in the Long Xuyen Quadrangle in particular, and the Vietnamese Mekong Delta as a whole.