The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East

Abstract

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in "mega" cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO₂ in Amman, Beirut, and Jeddah decreased by -56.6%, -43.4%, and -32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO₂ levels in megacities like Tehran (-0.9%) and Cairo (-3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

Keywords: Air quality; COVID-19; Climate change; Middle east; Urban heat island.

Graphical abstract

Fig. 1

The selected 21 major metropolitan cities in the Middle East.

Fig. 2

Changes (%) in the concentrations of air pollutants, calculated as the differences between these concentrations in 2020 and those of 2019. Differences were computed for each month independently during the lockdown period (i.e. March–June).

Fig. 3

Ranking the 21 metropolitans according to the score computed for each atmospheric pollutant. The right panel indicates the air quality aggregated index accounting for the full range of atmospheric pollutants.

Fig. 4

Anomalies of the mean intensity of daytime (red) and nighttime (blue) SUHI during the 2020 lockdown months, relative to those computed for the longer base period of 2003–2019. Anomalies were computed for each month independently. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Anomalies of the spatial extent of hotspot areas during daytime (red) and nighttime (blue) in the 2020 lockdown months, relative to those computed for the longer base period 2003–2019. Anomalies were computed for each month independently. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

Anomalies of the mean intensity of daytime (red) and nighttime (blue) SUHI during the 2020 lockdown months, relative to those of 2019. Anomalies were computed for each month independently. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7

Association between changes in the mean intensity of SUHI and spatial extent of hotspot area during the COVID-19 lockdown period in March–June 2020. Pearson's r correlation is computed for each month independently. Only numbers in bold are statistically significant at the 95% level (p < 0.05).