The impact of COVID-19 on NO 2 and PM 2.5 levels and their associations with human mobility patterns in Singapore
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SciScore for 10.1101/2021.12.02.21267165: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The reason for the positive correlation in the north may be due to the inherent limitations of the taxi availability. There are a few points in the north that have very limited number of taxis available (i.e., below 20 units per week), as well as the corresponding change, making the change in taxi availability there may not be …
SciScore for 10.1101/2021.12.02.21267165: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The reason for the positive correlation in the north may be due to the inherent limitations of the taxi availability. There are a few points in the north that have very limited number of taxis available (i.e., below 20 units per week), as well as the corresponding change, making the change in taxi availability there may not be representative for the mobility change. This spatial variation may also explain the insignificant correlation results observed in the regional analysis between PM2.5 and carpark availability changes. 3.5. Spatial autocorrelation of correlation coefficients: Spatial autocorrelation were run for each NO2 data point, as well as for each planning area using all observations within the corresponding planning area. The hot and cold spot analysis using the three correlation methods (Pearson, Spearman’s Rank and Kendall Rank) revealed generally similar spatial patterns (Figures 13). Notably, there is a clear north-south division in the statistically significant hot and cold spots, with the cold spots in the South and East Coast area, and hot spots in the north. For the non-parametric Spearman’s and Kendall correlations, the strength of the confidence in the south is slightly weaker, but still significant. This suggests that in the South and East Coast areas, a decrease in mobility represented by an increase in taxi availability could more possibly lead to a reduction in NO2 due to the cluster of stronger correlations compared to other areas.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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