Reduction in urban atmospheric CO2 enhancement in Seoul, South Korea, resulting from social distancing policies during the COVID-19 pandemic

Atmospheric CO2 in the megacity Hangzhou, China: Urban-suburban differences, sources and impact factors

Limited observation sites and insufficient monitoring of atmospheric CO2 in urban areas restrict our comprehension of urban-suburban disparities. This research endeavored to shed light on the urban-suburban differences of atmospheric CO2 in levels, diurnal and seasonal variations as well as the potential sources and impact factors in the megacity of Hangzhou, China, where the economically most developed region in China is. The observations derived from the existing Hangzhou Atmospheric Composition Monitoring Center Station (HZ) and Lin'an Regional Atmospheric Background Station (LAN) and the newly established high-altitude Daming Mountain Atmospheric Observation Station (DMS), were utilized. From November 2020 to October 2021, the annual averages of HZ, LAN and DMS were 446.52 ± 17.01 ppm, 441.56 ± 15.42 ppm, and 422.02 ± 10.67 ppm. The difference in atmospheric CO2 mole fraction between HZ and LAN was lower compared to the urban-suburban differences observed in other major cities in China, such as Shanghai, Nanjing, and Beijing. Simultaneous CO2 enhancements were observed at HZ and LAN, when using DMS observations as background references. The seasonal variations of CO2 at LAN and DMS exhibited a high negative correlation with the normalized difference vegetation index (NDVI) values, indicating the strong regulatory of vegetation canopy. The variations in boundary layer height had a larger influence on the low-altitude HZ and LAN stations than DMS. Compared to HZ and LAN, the atmospheric CO2 at DMS was influenced by emissions and transmissions over a wider range. The potential source area of DMS in autumn covered most areas of the urban agglomeration in eastern China. DMS measurements could provide a reliable representation of the background level of CO2 emissions in the Yangtze River Delta and a broader region. Conventional understanding of regional CO2 level in the Yangtze River Delta through LAN measurements may overestimate background concentration by approximately 10.92 ppm.

Machine learning based estimation of urban on-road CO2 concentration in Seoul

The urban on-road CO2 emissions will continue to increase, it is therefore essential to manage urban on-road CO2 concentrations for effective urban CO2 mitigation. However, limited observations of on-road CO2 concentrations prevents a full understanding of its variation. Therefore, in this study, a machine learning-based model that predicts on-road CO2 concentration (CO2traffic) was developed for Seoul, South Korea. This model predicts hourly CO2traffic with high precision (R2 = 0.8 and RMSE = 22.9 ppm) by utilizing CO2 observations, traffic volume, traffic speed, and wind speed as the main factors. High spatiotemporal inhomogeneity of hourly CO2traffic over Seoul, with 14.3 ppm by time-of-day and 345.1 ppm by road, was apparent in the CO2traffic data predicted by the model. The large spatiotemporal variability of CO2traffic was related to different road types (major arterial roads, minor arterial roads, and urban highways) and land-use types (residential, commercial, bare ground, and urban vegetation). The cause of the increase in CO2traffic differed by road type, and the diurnal variation of CO2traffic differed according to land-use type. Our results demonstrate that high spatiotemporal on-road CO2 monitoring is required to manage urban on-road CO2 concentrations with high variability. In addition, this study demonstrated that a model using machine learning techniques can be an alternative for monitoring CO2 concentrations on all roads without conducting observations. Applying the machine learning techniques developed in this study to cities around the world with limited observation infrastructure will enable effective urban on-road CO2 emissions management.

Influence of COVID-19 on the 10-year carbon footprint of the Nagoya University Hospital and medical research centre

BACKGROUND

Amidst the climate crisis, a key goal of the medical sector is to reduce its large carbon footprint. Although the Coronavirus disease 2019 (COVID-19) pandemic greatly impacted the medical sector, its influence on carbon footprints remains unknown. Therefore, the aim of this study was to evaluate changes in the carbon footprint of a university hospital with a medical research centre over the past 10 years.

METHODS

Data on electricity, gas, and water usage, pharmaceutical and medical supply costs, and waste amounts were recorded for Nagoya University Hospital from April 2010 to March 2021. The relevant emission factors were obtained from the Japanese government and the overall monthly carbon footprint was reported according to the Greenhouse Gas Protocol. The effect of the COVID-19 pandemic on the carbon footprint was then compared for three types of emission sources. Moreover, a regression model was used to plot quadratic functions as approximate functions using monthly carbon emissions and monthly average external temperatures. Finally, the monthly carbon footprint was calculated per hospital admission.

RESULTS

The overall carbon footprint of the hospital was 73,546 tCO2e in 2020, revealing an increase of 26.60% over the last 10 years. Carbon emissions from electricity consumption represented 26% of total emissions. The individual carbon footprints of pharmaceuticals, medical supplies, waste, and water usage also increased from 2010 to 2020. The overall monthly carbon footprint was positively correlated with the average monthly temperature (R2 = 0.7566, p < 0.001). Compared with 2019, the overall carbon footprint decreased by 2.19% in 2020. Moreover, the monthly carbon footprint per hospital admission increased significantly between 2018 (0.24 tCO2e/admission) and 2020 (0.26 tCO2e/admission) (p = 0.002).

CONCLUSION

The overall carbon footprint of the hospital generally increased over the last decade. During the COVID-19 epidemic in 2020, the carbon footprint decreased slightly, likely because of the reduced number of patients. However, the carbon footprint per admission increased, which was attributed to more complicated patient backgrounds because of the ageing population. Therefore, evaluation of carbon emissions in the medical sector is urgently required in order to act on the climate crisis as soon as possible.

Spatiotemporal variations in urban CO2 flux with land-use types in Seoul

BACKGROUND

Cities are a major source of atmospheric CO₂; however, understanding the surface CO₂ exchange processes that determine the net CO₂ flux emitted from each city is challenging owing to the high heterogeneity of urban land use. Therefore, this study investigates the spatiotemporal variations of urban CO₂ flux over the Seoul Capital Area, South Korea from 2017 to 2018, using CO₂ flux measurements at nine sites with different urban land-use types (baseline, residential, old town residential, commercial, and vegetation areas).

RESULTS

Annual CO₂ flux significantly varied from 1.09 kg C m^- 2 year^- 1 at the baseline site to 16.28 kg C m^- 2 year^- 1 at the old town residential site in the Seoul Capital Area. Monthly CO₂ flux variations were closely correlated with the vegetation activity (r = - 0.61) at all sites; however, its correlation with building energy usage differed for each land-use type (r = 0.72 at residential sites and r = 0.34 at commercial sites). Diurnal CO₂ flux variations were mostly correlated with traffic volume at all sites (r = 0.8); however, its correlation with the floating population was the opposite at residential (r = - 0.44) and commercial (r = 0.80) sites. Additionally, the hourly CO₂ flux was highly related to temperature. At the vegetation site, as the temperature exceeded 24 ℃, the sensitivity of CO₂ absorption to temperature increased 7.44-fold than that at the previous temperature. Conversely, the CO₂ flux of non-vegetation sites increased when the temperature was less than or exceeded the 18 ℃ baseline, being three-times more sensitive to cold temperatures than hot ones. On average, non-vegetation urban sites emitted 0.45 g C m^- 2 h^- 1 of CO₂ throughout the year, regardless of the temperature.

CONCLUSIONS

Our results demonstrated that most urban areas acted as CO₂ emission sources in all time zones; however, the CO₂ flux characteristics varied extensively based on urban land-use types, even within cities. Therefore, multiple observations from various land-use types are essential for identifying the comprehensive CO₂ cycle of each city to develop effective urban CO₂ reduction policies.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.