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1.
Heliyon ; 8(8): e09978, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1936477

ABSTRACT

This work analyzes nitrogen dioxide (NO2) pollution over a set of cities in the Po Valley in northern Italy, using satellite and in situ observations. The cities include Milan, Bergamo, and Brescia, the first area of the COVID-19 outbreak and diffusion in Italy, with a higher mortality rate than in other parts of Italy and Europe. The analysis was performed for three years, from May 2018 to April 2021, including the period of first-wave diffusion of COVID-19 over the Po Valley, that is, January 2020-April 2020. The study aimed at giving a more general picture of the NO2 temporal and spatial variation, possibly due to the lockdown adopted for the pandemic crisis containment and other factors, such as the meteorological conditions and the seasonal cycle. We have mainly investigated two effects: first, the correlation of NO2 pollution with atmospheric parameters such as air and dew point temperature, and second the possible correlation between air quality and COVID-19 deaths, which could explain the high mortality rate. We have found a good relationship between air quality and temperature. In light of this relationship, we can conclude that the air quality improvement in March 2020 was primarily because of the lockdown adopted to prevent and limit virus diffusion. We also report a good correlation between NO2 pollution and COVID-19 deaths, which is not seen when considering a reference city in the South of Italy. The critical factor in explaining the difference is the persistence of air pollution in the Po Valley in wintertime. We found that NO2 pollution shows a seasonal cycle, yielding a non-causal correlation with the COVID-19 deaths. However, causality comes in once we read the correlation in the context of current and recent epidemiological evidence and leads us to conclude that air pollution may have acted as a significant risk factor in boosting COVID-19 fatalities.

2.
Remote Sensing ; 12(14):2212, 2020.
Article | WHO COVID | ID: covidwho-654753

ABSTRACT

This work presents a regridding procedure applied to the nitrogen dioxide (NO2) tropospheric column data, derived from the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI). The regridding has been performed to provide a better comparison with punctual surface observations. It will be demonstrated that TROPOMI NO2 tropospheric column data show improved consistency with in situ surface measurements once the satellite retrievals are scaled to 1 km spatial sampling. A geostatistical technique, i.e., the ordinary kriging, has been applied to improve the spatial distribution of Level 2 TROPOMI NO2 data, which is originally sparse and uneven because of gaps introduced by clouds, to a final spatial, regular, sampling of 1 km ×1 km. The analysis has been performed for two study areas, one in the North and the other in the South of Italy, and for May 2018-April 2020, which also covers the period January 2020-April 2020 of COVID-19 diffusion over the Po Valley. The higher spatial sampling NO2 dataset indicated as Level 3 data, allowed us to explore spatial and seasonal data variability, obtaining better information on NO2 sources. In this respect, it will be shown that NO2 concentrations in March 2020 have likely decreased as a consequence of the lockdown because of COVID-19, although the far warmest winter season ever recorded over Europe in 2020 has favored a general NO2 decrease in comparison to the 2019 winter. Moreover, the comparison between NO2 concentrations related to weekdays and weekend days allowed us to show the strong correlation of NO2 emissions with traffic and industrial activities. To assess the quality and capability of TROPOMI NO2 observations, we have studied their relationship and correlation with in situ NO2 concentrations measured at air quality monitoring stations. We have found that the correlation increases when we pass from Level 2 to Level 3 data, showing the importance of regridding the satellite data. In particular, correlation coefficients of Level 3 data, which range between 0.50-0.90 have been found with higher correlation applying to urban, polluted locations and/or cities.

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