Air pollutants in pre-lockdown and during-lockdown periods. Health consequences
We have evaluated the behaviour of NO2, CO, SO2, H2S, O3, PM10 and PM2.5 along the four months of the study at each station of the network to determinate graphically how the lockdown has influenced the air quality. Besides, we have calculated the change in slope and quantified the percentage of variation of the concentrations before and after 14th March according with the type of area and source.
Nitrogen dioxide and carbon monoxide
Nitrogen dioxide is the most clearly reduced its concentration (global mean of -53.7%) in all stations [20]. Only three nearby industrial areas increased the level. NO2 is the typical pollutant emitted by car exhausts and energy production, in both NO and NO2 species, i.e. NOx [21]. Carbon monoxide is originated from similar sources [22] and its concentrations down by -27.0%.
For the whole region, starting from pre-lockdown levels (1st January – 13th March) the highest decreases produced from 14th March were obtained in stations of the province of Granada (-60.4% from 28.5 µg m-3), Sevilla (-62.4% from 18.9 µg m-3), Malaga (-65.0% from 21.4 µg m-3) and Jaen (-67.7% from 15.8 µg m-3), indicating that the highest reductions were observed for the highest pre-lockdown values. These changes are clearly shown by the SENTINEL-5P satellite measurements of tropospheric NO2 concentrations (µmol m-2) when comparing both periods (Figure 2, top): pre-lockdown values were around 50 µmol m-2 and much less than 45 µmol m-2 during lockdown. Observing the concentration-time plots for the most populated city, Sevilla, most stations have a decreasing trend of NO2 (Figure 3a), being reduction rates from -55.4% (Torneo) to -72.0% (Alcala), with rapid trafficked roads. Similar results were found in the Lombardia Region where only in a heavy traffic zone NO2 showed a significant decrease [23]. The rest of urban and suburban stations decreased over 60.4%. rural stations the percentages were -24.2% in Gerena and -37.9% in Sierra Norte, both located more than 50 km from the city, so the rate of reduction decreases the more distance from the big city. The behaviour of NO2 in the rest of cities is shown in Figure 3b. Some representative reduction rates were -66.1% (in Pozo Dulce, the centre of Huelva), -68.4% (in Marbella, a city of Malaga Province) and -81.3% (in San Fernando, a city of Cadiz Province), having all of them high traffic densities and nearby industrial activities (Huelva and Cadiz), as it was also reported by other studies [24, 25, 26).
Due to the direct relationship between the concentration of NO2 in the air and hospital admissions for cardiovascular disease [27], the health consequences of the NO2 reduction were extremely beneficial, such as the lower number of acute hospital admissions for cardiovascular disease in the short-term (acute ischemic syndromes, atrial fibrillation, and decompensated heart failure [28], and possibly a lower cardiovascular mortality due to environmental causes in the long run.
Regarding carbon monoxide, stations decreased its concentration after lockdown and others increased it, as it was reported in USA [19], Italy [29] and by interdisciplinary studies developed for 40 cities [30]. The highest decreases starting from the pre-lockdown values were observed in stations of Sevilla (-45.0% from 477 µg m-3), Jaen (-48.7% from 432 µg m-3), Cadiz (-52.6% from 522 µg m-3) and Huelva (-58.2% from 705 µg m-3), with values two times higher than in Cordoba (-32.7% from 370 µg m-3) and Granada (-33.0% from 363 µg m-3). Pre-lockdown levels in the period 1st January-13 March 2020 were similar to the 2019 values for the same stations and period. Thus, the highest decreases were also related to high pre-lockdown concentrations, as in the case of NO2. In conclusion, for both NO2 and CO the highest reduction tases were in stations where pre-lockdown levels are elevated. These stations were near to the two most important industrial areas, the Chemical Park of Huelva, and the Campo de Gibraltar of Cadiz, but also in cities with high rate of vehicles circulation, the province of Sevilla. These results are observed by the SENTINEL-5P satellite measurements of CO concentrations when comparing both periods (Figure 2, bottom): pre-lockdown values were between 106 and 129 µmol m-2, and between 60 and 83 µmol m-2 during lockdown.
In the case of carbon monoxide the health consequences of the CO reduction are not clear because no direct relation between CO at low-medium concentrations and hospital admissions are usually found by researchers [31]. Some authors found direct association with negative effects of short-term CO exposure only over 2,000 µg m-3 [32].
The reduction rates of CO in Sevilla were from -24.8% in Bermejales (-63.1% NO2) to -50.0% in Torneo (-55.4% NO2)(Figure 3c). Curiously, within the lockdown period we have observed a small and unexpected period of increase inside the decreasing profile. This coincides with the Easter holiday period (6th April), which suggest the departures of private vehicles from the city despite of being forbidden for citizen. When the behaviour of CO in the rest of cities was decreasing (Figure 3d) the stations with the most reduction rates had high traffic densities, such as Pozo Dulce (in Huelva, -70.8% CO, -66.1% NO2), Jerez (in Cadiz, -83.4% CO, -71.0% NO2), Lepanto (in Cordoba, -64.4% CO, -79.9% NO2), Fuentezuelas (in Jaen, -52.0% CO, -68.6% NO2) and Congresos (in Granada, -56.1% CO, -48.7% NO2).
On the other hand, if a lockdown consists of a total braking of all vital activities, an increase in CO should indicate an increase in ‘essential’ economic activities that cannot be stopped. In the province of Sevilla, the increases corresponded to stations located near major industrial areas (see above google maps links): Gerena (+35.8% CO, -24.2% NO2) is next to a big copper mine. Similarly, three stations close to each other, Ranilla (+25.0% CO, -69.2% NO2), Alcala (+16.5% CO, -71.8% NO2) and Dos Hermanas (+19.4% CO, -71.4% NO2) receive the influence of a big stainless steel factory, two detergent plants and a cement factory. In addition, the CO profile presented a continuous increase in the form of ‘sawtooth cycles’ (Figures 3e and 3f). This type of variation was also observed for aerosol particles [33] and for particles and nitrogen dioxide. In our case, observing these cycles we found that they occured coinciding with rainfall episodes as it can see in Figure 3e.
In the rest of cities the increases of CO corresponded to stations Joya, Cartuja, Barrios and Marconi (Figure 3f). Joya (Almeria, +43.2% CO, +16.6% NO2) and Barrios (Cadiz, +27.7% CO, -37.0% NO2) are next to coal thermal power plants, and Barrios is near the first major stainless steel factory of Andalusia (see above google maps links). Cartuja (in Jerez de la Frontera, Cadiz, +26.6% CO, -79.6% NO2) is next to the industrial zone of the city and Marconi (Cadiz, +25.1% CO, -85.0% NO2) is next to the ‘Zona Franca’ industrial area and the ‘Bay of Cadiz’ Port Area. Consequently, differences between decreases and increases of carbon monoxide concentrations were caused by the increasing activity in power generating industries and others essential industries during the lockdown period.
Sulfur dioxide and hydrogen sulfide
Sulfur dioxide emissions come mainly from industrial activities [34], however no clear trend was observed in the evolution of SO2, as it was reported in Barcelona (Spain) [35]. Regarding H2S, emissions can come from incomplete combustions and from wastewater [36]. H2S emissions increase on lockdown in practically all stations where the gas sensor is installed. In the majority of stations no significant variations were observed for SO2, except in some places with extreme values (Figure 3h), such as in Ejido (Almeria), in Moguer (Huelva), in Obejo and Villaharta (Cordoba) and in Gerena (Sevilla).
Ejido (+190.5% in SO2, -79.0% in NO2) has no relevant industries, but it contains the largest extension of plastic greenhouses in Spain (‘sea of plastic’), and H2S also increased +103.2%. Greenhouse plastics are usually degraded by elemental sulfur, which is used to disinfect greenhouses, which is why commercial plastics for greenhouses are manufactured protected against sulfur. So, these extreme values are due to burning of used plastics mixed with organic residues. Also, in Moguer, next to the petrol refinery in Huelva Chemical Park, an increase of +84.7% in SO2 was observed (-56.2% in NO2), similar to those reported in UK [37, 38]. Obejo and Villaharta are near a coal thermal power plant, and the values increased too much for SO2 (+82.6% and +92.3%) and for H2S (+84.3% and +32.5%) after 14th March. Villaharta also receive NO2 emissions (+34.7%) probably from the chimney of the power plant (see above google maps link). Regarding Gerena, the mine zone showed an increase of +67.9% in SO2 in comparison with a decrease of -24.2% in NO2. All these extreme increases in SO2 levels must be attributed to the increase in burning of coal [39] or other fossil fuels by thermal power plants [40] or other industrial activities, such as mines or refineries. According Wang et al. [25], concentrations of SO2 and other pollutants such as CO and NO2 appeared to have positive effect on hospitalizations, especially at high concentrations, for instance by bronchiectasis. Also other authors [41] reported that only sulfur dioxide was associated with out-patient visits. Associations of SO2 and CO with pneumonia were reported [42]. Consequently, it would be advisable to observe especially these unusual patterns to alert the population, although they are less frequent than the general decreases observed for the studied air pollutants.
Regarding decreases of sulfur dioxide concentrations Figure 3g shows higher differences between levels before and after 14th March. Stations such as Ranilla (Sevilla, -46.0% SO2, -69.2% NO2), Algeciras (Campo de Gibraltar, -51.7% SO2, -21.3% NO2) and Rosales (Huelva, -55.4% SO2, -63% NO2) were located in residential places of industrial areas, suggesting the stopping of private and economic activities. Similar results were obtained in Milan (Italy) [28].
Ozone and Particulate matter (PM10 and PM2.5)
In 2018, 1,206 exceedances of threshold limits [43] were reported, which 979 were due to tropospheric ozone, 224 to particulate matter, 3 to nitrogen dioxide and none to sulphur dioxide.
Ozone
The atmospheric conditions most suitable for achieving high levels of this secondary pollutant are the result of the reactions of nitrogen oxides (NOx) with carbon monoxide, methane and non-methane volatile organic compounds (NMVOCs), i.e. their precursors, under high solar radiation and temperatures [44]. Excessive ozone in the air can have a marked effect on human health. It can cause breathing problems, trigger asthma, reduce lung function and cause lung diseases [45].
In connection with our study, we observed that the concentration of ozone increased during the lockdown in all monitoring stations. In this sense, the increase was related to the fall of NO2 and CO, because of O3 concentrations were negatively correlated with NO2, CO and also PM2.5 and PM10. The increase is attributed in almost all studies to the decrease of NO2 (NOx or NO) and VOCs [46]. The highest increases occur in traffic stations where the pre-lockdown levels of ozone are low. It is certainly not normal that whilst in most stations nitrogen oxides decreased, they increased in parallel ozone levels. Therefore we propose an explanation: if under certain conditions the reduction of NO2 emissions can involve an increase in ozone levels, a process well known as ‘Ozone Weekend Effect (OWE)’ [47, 48], we asked ourselves, Could the weekend effect explains similarly the increase in ozone during lockdown? Our hypothesis affirm: “despite the decrease in the levels of its main precursors, NO2 and CO, the increase in ozone levels during lockdown is explained by an effect that we will be calling the 'Ozone Lockdown Effect (OLE)', similar to the 'weekend effect' but longer in time”. Other similar study developed in four cities from Southern Europe, including Valencia (Spain), also reported a similar concept, the ‘lockdown effect on O3 production’ as the increase of ozone during lockdown for a longer period [49].
In terms of health effects, some authors declare that no significant relationship between tropospheric ozone and hospital admissions was found for respiratory diseases [50]. However, the majority of studies found associations with respiratory and cardiovascular affections [27, 51, 52], such as pneumonia and COPD, chronic obstructive pulmonary disease [53].
If we make a joint study for CO, NO2, O3 before/after 14th March in Sevilla, the levels of NO2 were reduced by up to -19 µg m-3 in Centro (historical Centre of Sevilla), Torneo and Ranilla, while the maximum reduction of CO reached -401 µg m-3 in Torneo, Ranilla and also Santa Clara (in the border of Sevilla). As ozone was negatively correlated with NO2 and CO (Figures 4a and 4b) the increases of O3 of up to 40 µg m-3 (Figure 5a) occurred when pre-lockdown levels of ozone were significantly low, such as in Torneo (+41.2 µg m-3, initially from 10.8 µg m-3), Santa Clara (+40.1 µg m-3, initially from 8.2 µg m-3) and Centro (+36.2 µg m-3, initially from 13.3 µg m-3), being all of areas with usual high vehicle traffic, which emit the ozone precursors NOx, CO and also VOCs [54]. In areas where pre-lockdown levels of O3 are significantly high, the Ozone Lockdown Effect is not so marked, and the increases were significantly low (Figure 5a), as it can see in Sierra Norte (+19.1 µg m-3, initially from 35.1 µg m-3), Gerena (+15.1 µg m-3, initially from 26.0 µg m-3) and Alcala (+24.8 µg m-3, initially from 33.3 µg m-3), being all far from the city with low traffic in background or rural areas, some of these near industries (Gerena). Furthermore, it would be interesting to study if the ozone lockdown effect is also visible in provinces with cities many industrialised, that is, in Huelva and Cadiz.
So, in these cases, the highest ozone increases were also related with low pre-lockdown values, being the most representative stations Punta Umbria (a coastal city near Huelva, +32.1 µg m-3, initially from 25.1 µg m-3) and Matalascañas (a coastal city near Huelva, +31.3 µg m-3, initially from 28.4 µg m-3), both in Huelva province, and Marconi (Figure 5b, +43.2 µg m-3, initially from 34.1 µg m-3) and Jerez (+33.0 µg m-3, initially from 29.1 µg m-3) in Cadiz province. These monitoring stations are located in very touristic coastal cities, very transited before the lockdown. On the opposite side, the ‘ozone lockdown effect’ is not so remarkable in stations with high pre-lockdown levels, such as Rabida (+10.1 µg m-3, initially from 41.2 µg m-3) and Moguer (+18.2 µg m-3, initially from 40.1 µg m-3) near the Chemical Park of Huelva, and Arcos (+13.0 µg m-3, initially from 69.9 µg m-3) and Campamento (Figure 5b, in the ‘Campo de Gibraltar’, +16.2 µg m-3, initially from 78.9 µg m-3) in Cadiz. Thus, ozone formation is more important in trafficked areas [52] where the reduction of road traffic is more significant than in those areas where the reduction occurs near industrial activities.
These results confirm our hypothesis and thus, despite the decrease of NO2 and CO, the increase of tropospheric ozone during the lockdown constitutes the 'ozone lockdown effect, OLE'. In addition, in the 4-month period studied, the 'ozone weekend effect, OWE’ was observed in most of the weekly periods of all stations.
PM10 and PM2.5
As mentioned above, the poor air quality was mainly due to particles and ozone. Nowadays, particulate matter is a primary pollutant that has a negative correlation with ozone [45] because of aerosols can have a radiation effect through both absorption and scattering [55, 56]. Furthermore, other authors propose that aerosols reduced the actinic flux photolysis rates under absorption and scattering, so the surface ozone production decreases [57, 58]. Li et al. [59] demonstrated that O3 was strongly inhibited under the condition of high PM2.5 concentration. In addition, as in the case of CO and NO2, our correlation study shows also high negative correlation coefficient between O3 and PM10 PM2.5 (Figures 4c and 4d).
In the province of Sevilla, particle concentrations decreased in all the areas, particularly in stations located inside the city, Torneo and Principes, with higher PM10 and PM2.5 decreases than stations far from the city (Figure 5c), Gerena and Sierra Norte. The magnitude of the decrease depends on the pre-lockdown values, i.e., the greatest decreases occur in stations with higher pre-lockdown concentrations (23 - 24 µg m-3), all of them trafficked stations. Additionally, if we compare between concentrations of PM10 and PM2.5, the result shows that the decrease in PM2.5 levels (-57%) in lockdown were higher than in PM10 (-42.0%), suggesting and corroborating the much more anthropogenic contribution (Figure 5d).
In Cadiz (Figure 5e) and Huelva (Figure 5f), levels of PM decrease and increase also. In Cadiz, the greatest decreases in PM10 levels occur in urban stations with a high traffic, such as Jerez (-42.5%) and Marconi (-28.1%). Rural and background areas, such as the Alcornocales National Park, and the ‘Arcos de la Frontera’ city, show low decreases, -7.3%, while in industrial areas PM10 seems to decline very lightly, such as in Rinconcillo (+5%), Linea and Palmones (-5%) or even rising in Zabal (+14%), all them near a big stainless steel factory and a big petrol refinery of the Bay of Algeciras. In Huelva, the same perceptions were observed. The greatest decreases in PM10 occur within the city of Huelva, where the most intense urban traffic was stopped at 14th March. Titan (-32%), Rosales (-29%), Pozo Dulce (-25%) and Carmen (the University Campus, -21%) were the representative stations, whereas inside industrial areas, Palos and Moguer (-14%), San Juan and Rabida (-11%, -9%) and the coastal areas, Punta Umbria (-10%), Mazagon (0%) and Matalascañas (+8%) had lower decreases or even increased the particle concentrations.
In conclusion, these results in Sevilla, Cadiz and Huelva reinforce the idea that since ozone and PM10/PM2.5 are negatively correlated and traffic zones suffer the greatest reductions, we must consider PMs as further variables involved in the 'ozone lockdown effect OLE' like CO and NO2. Some authors found that during lockdown the ozone concentrations were higher than in normal situations, being this related with the increase of organic carbon and decrease of elemental carbon in aerosols [60].
Nevertheless, if ozone is the most irritating gas pollutant, it may be assumed that its presence could influence the incidence of the COVID-19 disease. Therefore, can it be assumed that there is an important relationship between airborne ozone levels and the number of COVID-19 infections or deaths?
Correlation study for air pollutants: relationship with the incidence of COVID-19. Meteorological and traffic implications
As mentioned above, correlation studies were done between atmospheric and meteorological variables, traffic densities and also with number of infections and deaths due to COVID-19 disease. The study was done for all representative locations in the region. In the case of traffic densities the study was cantered in Sevilla, the most trafficked city.
Ozone and number of infections and deaths by COVID-19
On the other hand, we have detected an interesting relation between daily infections/daily deaths and ozone concentration. The correlation coefficient between ozone and number of infections was r = + 0.71 (Figure 6a): when ozone is high the number of infections is also elevated and when ozone decreases the infection cases also was reduced.
In addition, Figure 6b shows how infections cases in Sevilla increased 1-2 days after the ozone concentration rise. The possibility of a coincidental parallel increase of ozone and infection cases is not plausible because the profile also shows a period from 1st April where decreasing infections is preceded by decreasing ozone. In order to check this behaviour in other important cities outside the Southern Spain, we also studied this relationship in the cities of Madrid and Barcelona (Figures 6c and 6d), corroborating the behaviour observed in Sevilla: in graphics b), c) and d), every peak/valley of the infections black lines (INF) is preceded by a peak/valley of the ozone red lines (O3) (see grey lines), even in the both rising and decreasing periods. This fact confirms our hypothesis, which states that the presence of high levels of ozone is a determinant factor on the incidence of COVID-19 disease, further reducing the infection rate when the ozone level decreases. This behaviour constitutes an effect that we will be calling the 'Ozone COVIDEffect (OCE)'.
The number of deaths was also correlated with the ozone level (r = + 0.59), so that, despite the high rate of person-to-person transmission of the new COVID-19 virus, the high oxidizing character of ozone constitutes the first ambient factor that enhances the incidence of the COVID-19. As a result, we propose a mechanism for infected people, in which the ozone entering into the lungs accelerates the occurrence of the virus symptoms, even in healthy people, increasing the seriousness of the disease.
Air pollutants and meteorological parameters
Firstly, regarding Pearson correlations studied (Table 2), we confirmed the well known relationships along air pollutants, such as mentioned above for ozone, O3 with NO2, O3 with CO, ozone with PM10, ozone with PM2.5 and for nitrogen dioxide, NO2 with CO, NO2 with PM10, NO2 with PM2.5 or CO with PM10. No significant correlations were found for SO2 (r < ± 0.32). Other well known correlations were also found between meteorological parameters and air pollutants, for instance those for atmospheric pressure (AP), such as AP with O3 (Figure 7a), AP with NO2, AP with CO or AP with PM10, those for wind speed (WS), such as WS with O3, WS with NO2 or WS with CO (Figure 7b), or those for air temperature (AT), such as AT with O3 or AT with NO2 (Figure 7c).
Correlations with rain should be performed in a different way: a significant value of precipitation (RF > 0.4 mm) implies an air-cleaning effect the next day, which is why concentrations of one day need to be correlated with that of the following. So, correlations found (Table 2) for the rainfall (RF) were for CO, RF with SO2, RF with NO2 and RF with PM10 (Figure 7d), but no correlation was found for ozone. This way, these correlations indicate a gas/particles-cleaning effect, being possible a dissolving effect in the raindrops and thus obtaining rainwater with nitrate or sulphate in solution [61]. Thus, we have seen above that increases of CO in sawtooth form of Figure 3e were related to the correlation with rain events (Table 2). A particle-cleaning effect is also produced by rain events [62]. Regarding infection cases and deaths, atmospheric pressure showed negative correlations with cases of infections and deaths, AP with INF (r = - 0.88), AP with DEA (r = - 0.71), and positives with temperature, AT with INF (r = + 0.73), AP with DEA (r = + 0.51).
Air pollutants and traffic of vehicles
The correlation study also included the vehicular traffic densities (VTDs) in the city of Sevilla, which should be correlated with air pollutants. Thirty-three street and roads routes were included. Since the main consequence of the lockdown was the reduction of traffic densities, the correlation study compared Pearson coefficients before 14th March and after 13th March, as was studied by Hashim et al. [24].
The results showed that Pearson coefficient for VTD/NO2 was higher before the lockdown than after it (r = + 0.72 < 14th March, r = + 0.59 > 13th March, NO2, Figures 7e, 7f) but for ozone it was lower before (r = - 0.47 < 14th March, r = - 0.63 > 13th March, O3, Figures 7g, 7h). This fact confirms the regular and major transit of cars into pre-lockdown, and during lockdown, the much minor traffic and the ozone lockdown effect. What is more, the result is different in the case of carbon monoxide, where Pearson coefficient for VTD/CO is positive (r = + 0.53 < 14th March) within pre-lockdown, as NO2, but negative and higher (r = - 0.62 > 13th March) into lockdown, as O3, confirming the rise of road transport along industrial areas. No correlation was found for SO2.