TABLE 1.
A Database | ||||
---|---|---|---|---|
A1. | ScienceDirect | |||
A2. | Scopus | |||
B Search criteria | ||||
B1. | Journal | All | ||
B2. | Year | All | ||
B3. | Article type | All | ||
B4. | Date of search | 10th November 2020 | ||
C Keywords for papers identification | ||||
Group A | Group B | |||
COVID‐19 | Air pollution | |||
Lockdown | Air quality | |||
SARS‐CoV‐2 | AND | Environmental impact | ||
NO2 | ||||
PM10 | ||||
D Steps for material selection | ||||
D1. | Duplicate removal | |||
D2. | Keywords and highlights assessment | |||
D3. | Application of inclusion criteria on the content of the text | |||
D3.1 | Treats the air quality of specific areas | OR | ||
D3.2 | Assess the effect of COVID‐19 | OR | ||
D3.3 | It evaluate the results quantitatively | |||
D4. | Full text assessment | |||
E Other paper sources | ||||
E1. | From informal approach | |||
E2. | From browsing method | |||
F Descriptive analysis | ||||
Year | ||||
Journal | ||||
Country type | ||||
Research paper | ||||
Review | ||||
Others | ||||
G Case studies analysis | ||||
Location of the case studies | Location of the case studies | |||
Impact of lockdown on logistics and industrial/economic activities | Impact of lockdown on logistics and industrial/economic activities |