ABSTRACT
The COVID-19 pandemic has caused several millions of deaths and forced the world population to a new normality. This study aims to analyze the air quality variation of several gaseous pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) during the pre-lockdown, lockdown, and unlock period in the city of Monterrey using ground-based measurements. In this research, we proposed to use a control period of previous years to identify parameter variation due to local climate. The results showed a drastic decrease in measured contaminants during the lockdown period as follows: SO2 (-41.9%) > PM10 (-30.5%) > PM2.5 (-25.6%) > NO2 (-14.9%) > CO (-9.8%) compared to the control period (2017-2019). The O3 was the only air pollutant that showed an opposite trend, increasing during lockdown (+15%) and unlock (+2.2%), whereas CO (-16.6%) and NO2 (-30.6%) were further decreased. Moreover, using OMI/AURA satellite data, we detected a NO2 tropospheric column reduction by -1.9% during lockdown concerning the same period in the control interval. Moreover, we found a significant improvement in the Air Quality Index (AQI) due to the lockdown. Our findings indicate an association between air pollutants and economic activity and can be used in future strategies to improve urban air quality.
ABSTRACT
The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.