ABSTRACT
The atmospheric CO2 concentrations are mainly influenced by regional sinks/sources and atmospheric transport processes, thus observations in urban area contain essential information of anthropogenic CO2 emissions. To investigate the effect of COVID-19 on atmospheric CO2 concentration and its anthropogenic emissions, this study chose Nanchang city as the study area and used a priori emission inventory with WRF-STILT (Stochastic Time-Inverted Lagrangian Transport) atmospheric transport model to simulate hourly CO2 concentrations from January 24th to April 30th, 2020. In accordance with the government measures to control COVID-19 epidemic, the whole study period was divided into two periods of Level 1 period (from January 24th to March 11th) and Level 2 period (from March 12th to April 30th). Results indicate the model can well capture hourly variations of CO2 concentration, but it overestimated nighttime concentrations due to the negligence of emission source height. During Level 1 period, the observed and simulated afternoon (12:00-18:00) CO2 mole fractions were 433. 63×10-6 and 438. 22×10-6, respectively,in which the anthropogenic emissions were 21.9% overestimated by simulation compared with observations. While during Level 2 period, the observation and simulation were very close as 432. 06×10-6 and 432. 24 × 10-6. The above comparisons indicate that the CO2 emissions can be represented by a priori CO2 emission inventory in Level 2 period, but was overestimated by 21.9% in Level 1 period, and the discrepancy was mainly due to government measures to control COVID-19 pandemic during this period. Besides, the average biological NEE enhancements were generally lower than 2×10-6, indicating a small contribution compared with anthropogenic emissions. The higher PBLH (Planetary Boundary Layer Height) in Level 2 period also offset the enhancement in CO2 emissions, which was also the main reason for the close observations during two periods. Our findings can provide scientific method supports for greenhouse gas emission inversions at urban scale.
ABSTRACT
SARS-CoV-2 is one of the greatest threats to global human health. Point-of-care diagnostic tools for SARS-CoV-2 could facilitate rapid therapeutic intervention and mitigate transmission. In this work, we report CRISPR-Cas13a cascade-based viral RNA (Cas13C) assay for label-free and isothermal determination of SARS-CoV-2 and its mutations in clinical samples. Cas13a/crRNA was utilized to directly recognize the target of SARS-CoV-2 RNA, and the recognition events sequentially initiate the transcription amplification to produce light-up RNA aptamers for output fluorescence signal. The recognition of viral RNA via Cas13a-guide RNA ensures a high specificity to distinguish SARS-CoV-2 from MERS-CoV and SARS-CoV, as well as viral mutations. A post transcription amplification strategy was triggered after CRISPR-Cas13a recognition contributes to an amplification cascade that achieves high sensitivity for detecting SARS-CoV-2 RNA, with a limit of detection of 0.216 fM. In addition, the Cas13C assay could be able to discriminate single-nucleotide mutation, which was proven with N501Y in SARS-Cov-2 variant. This method was validated by a 100% agreement with RT-qPCR results from 12 clinical throat swab specimens. The Cas13C assay has the potential to be used as a routine nucleic acid test of SARS-CoV-2 virus in resource-limited regions.