Scientific evidence suggests that Saharan dust intrusions in Southern Europe contribute to the worsening of multiple pathologies and increase the concentrations of particulate matter (PM) and other pollutants. However, few studies have examined whether Saharan dust intrusions influence the incidence and severity of COVID-19 cases. To address this question, in this study we carried out generalized linear models with Poisson link between incidence rates and daily hospital admissions and average daily concentrations of PM 10 , NO 2 , and O 3 in nine Spanish regions for the period from February 1, 2020 to December 31, 2020. The models were adjusted by maximum daily temperature and average daily absolute humidity. Furthermore, we controlled for trend, seasonality, and the autoregressive nature of the series. The variable relating to Saharan dust intrusions was introduced using a dichotomous variable, NAF, averaged across daily lags in ranges of 0-7 days, 8-14 days, 14-21 days, and 22-28 days. The results obtained in this study suggest that chemical air pollutants, and especially NO 2 , are related to the incidence and severity of COVID-19 in Spain. Furthermore, Saharan dust intrusions have an additional effect beyond what is attributable to the variation in air pollution; they are related, in different lags, to both the incidence and hospital admissions rates for COVID-19. These results serve to support public health measures that minimize population exposure on days with particulate matter advection from the Sahara.

The analysis of maximum precipitation is usually carried out by using IDF curves (IntensityDuration-Frequency), which in turn could be expressed as MAI curves (Maximum Average Intensities). An index "n" has been developed in this work, defined from the exponent obtained when adjusting IDF climatic curves to MAI curves. That index provides information about how maximum precipitation is achieved in a certain climatic area, according to the relative temporal distribution of maximum intensities. From the climatic analysis of index "n", large areas could be distinguished in the Iberian Peninsula, characterized by rain maxima of a stormier origin (peninsular inland), and areas characterized by rain maxima of a more frontal origin (southwest, Atlantic coast and Mediterranean coast). Additionally, these areas could be more specifically divided according to the persistence of maximum precipitation.

Background There are studies that analyze the role of meteorological variables on the incidence and severity of COVID-19, and others that explore the role played by air pollutants, but currently there are very few studies that analyze the impact of both effects together. This is the aim of the current study. We analyzed data corresponding to the period from February 1 to May 31, 2020 for the City of Madrid. As meteorological variables, maximum daily temperature (Tmax) in ºC and mean daily absolute humidity (AH) in g/m3 were used corresponding to the mean values recorded by all Spanish Meteorological Agency (AEMET) observatories in the Madrid region. Atmospheric pollutant data for PM10 and NO2 in µg/m3 for the Madrid region were provided by the Spanish Environmental Ministry (MITECO). Daily incidence, daily hospital admissions per 100.000 inhabitants, daily ICU admissions and daily death rates per million inhabitants were used as dependent variables. These data were provided by the ISCIII Spanish National Epidemiology Center. Generalized linear models with Poisson link were performed between the dependent and independent variables, controlling for seasonality, trend and the autoregressive nature of the series. Results The results of the single-variable models showed a negative association between Tmax and all of the dependent variables considered, except in the case of deaths, in which lower temperatures were associated with higher rates. AH also showed the same behavior with the COVID-19 variables analyzed and with the lags, similar to those obtained with Tmax. In terms of atmospheric pollutants PM10 and NO2, both showed a positive association with the dependent variables. Only PM10 was associated with the death rate. Associations were established between lags 12 and 21 for PM10 and between 0 and 28 for NO2, indicating a short-term association of NO2 with the disease. In the two-variable models, the role of NO2 was predominant compared to PM10. Conclusions The results of this study indicate that the environmental variables analyzed are related to the incidence and severity of COVID-19 in the Community of Madrid. In general, low temperatures and low humidity in the atmosphere affect the spread of the virus. Air pollution, especially NO2, is associated with a higher incidence and severity of the disease. The impact that these environmental factors are small (in terms of relative risk) and by themselves cannot explain the behavior of the incidence and severity of COVID-19.

Resumen: Los extremos climáticos se han incrementado en España a los largo del último siglo; por ello, su análisis se ha convertido en una línea prioritaria de conocimiento con objeto fundamental de diseñar planes para la gestión y mitigación de sus efectos. Los datos de satélite permiten analizar las variaciones en la actividad de la vegetación a varias escalas temporales y su respuesta a la variabilidad climática. En este trabajo se pone de manifiesto la vulnerabilidad de la vegetación en España ante condiciones ambientales extremas a través de las correlaciones entre índices meteorológicos de sequía (SPI) y variables biofísicas extraídas de datos MODIS/EOS y SEVIRI/MSG. Las anomalías en la vegetación, como indicadores de las condiciones de humedad de la misma, pueden ayudar a cuantificar y gestionar episodios meteorológicos extremos y hacer un seguimiento de la misma. Las mayores correlaciones se han obtenido en las regiones áridas y semiáridas y durante los meses de máxima actividad de la vegetación, generalmente entre mayo y junio.

This study aims to identify the combined role of environmental pollutants and atmospheric variables at short term on the rate of incidence (TIC) and on the hospital admission rate (TIHC) due to COVID-19 disease in Spain. This study used information from 41 of the 52 provinces of Spain (from Feb. 1, 2021 to May 31, 2021). Using TIC and TIHC as dependent variables, and average daily concentrations of PM 10 and NO 2 as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). Generalized linear models (GLM) with Poisson link were carried out for each provinces The GLM model controlled for trend, seasonalities, and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 μg/m 3 in PM 10 and NO 2 and by 1 °C in the case of Tmax and 1 g/m 3 in the case of HA. Later, a linear regression was carried out that included the social determinants of health. Statistically significant associations were found between PM 10 , NO 2 , and the rate of COVID-19 incidence. NO 2 was the variable that showed greater association, both for TIC as well as for TIHC in the majority of provinces. Temperature and HA do not seem to have played an important role. The geographic distribution of RR in the studied provinces was very much heterogeneous. Some of the health determinants considered, including income per capita, presence of airports, average number of diesel cars per inhabitant, average number of nursing personnel, and homes under 30 m 2 could explain the differential geographic behavior. As findings indicates, environmental factors only could modulate the incidence and severity of COVID-19. Moreover, the social determinants and public health measures could explain some patterns of geographically distribution founded. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-19232-9.