key: cord-0910767-ectsjw51
authors: Briz-Redón, Álvaro; Belenguer-Sapiña, Carolina; Serrano-Aroca, Ángel
title: A city-level analysis of PM(2.5) pollution, climate and COVID-19 early spread in Spain
date: 2022-01-06
journal: J Environ Health Sci Eng
DOI: 10.1007/s40201-022-00786-2
sha: c29e90ef429364c3ed4936e3ac516e1ccf3a77be
doc_id: 910767
cord_uid: ectsjw51

PURPUSE: The COVID-19 outbreak has escalated into the worse pandemic of the present century. The fast spread of the new SARS-CoV-2 coronavirus has caused devastating health and economic crises all over the world, with Spain being one of the worst affected countries in terms of confirmed COVID-19 cases and deaths per inhabitant. In this situation, the Spanish Government declared the lockdown of the country. METHODS: The variations of air pollution in terms of fine particulate matter (PM(2.5)) levels in seven representative cities of Spain are analyzed here considering the effect of meteorology during the national lockdown. The possible associations of PM(2.5) pollution and climate with COVID-19 accumulated cases were also analyzed. RESULTS: While the epidemic curve was flattened, the results of the analysis show that the 4-week Spanish lockdown significantly reduced the PM(2.5) levels in only one city despite the drastically reduced human activity. Furthermore, no associations between either PM(2.5) exposure or environmental conditions and COVID-19 transmission were found during the early spread of the pandemic. CONCLUSIONS: A longer period applying human activity restrictions is necessary in order to achieve significant reductions of PM(2.5) levels in all the analyzed cities. No effect of PM(2.5) pollution or weather on COVID-19 incidence was found for these pollutant levels and period of time. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-022-00786-2.

Due to the growing COVID-19 pandemic, the government of Spain declared a lockdown on March 14, 2020. Extraordinary control policies were executed in an effort to reduce the COVID-19 transmission. At the beginning of the lockdown, from March 15 to March 29, 2020, named here as minor lockdown, people were recommended to work from home, and other measures such as travel restrictions, isolation, and quarantine of patients, cancellation of private and public events, online education, closing of restaurants, bars and pubs, or the prohibition of public congregations, were imposed. During those two weeks, only essential products could be sold in supermarkets and drugstores, and only a small amount of activities were allowed. During the last two weeks of the lockdown, from March 30 to April 12, 2020, named here as major lockdown, the Spanish Government imposed more severe restrictions due to the national emergency caused by the collapse of the health system. This situation forced people to stay at home (except for very limited purposes) and only essential work such as healthcare and social care sectors, police and armed forces, water and electricity supply was allowed. Besides, important industrial activities such as construction were forbidden. These unprecedented measures gave positive results and flattened the epidemic curve after a month of lockdown [1] . The impact of particulate matter (PM) on health is well-known Álvaro Briz-Redón and Carolina Belenguer-Sapiña contributed equally to this work. [2] [3] [4] in terms of its effects on morbidity and mortality. Two sizes of particulate matter are used to analyze air quality; fine particulate matter or PM 2.5 , with a diameter of 2.5 μm or less, and coarse particles or PM 10, with a diameter of 10 μm or less. However, the former is more worrying because their small size allows them to penetrate deeper into the human respiratory system via inhalation, which can potentially promote respiratory diseases [5, 6] such as COVID-19. Thus, it is of note that citizens exposed to a high concentration of PM 2.5 are more prone to developing chronic respiratory diseases favorable to infective agents [7] . Long-term exposure to these small particles can produce a chronic inflammatory stimulus, especially in unhealthy people [8] .

In addition, short-term exposure to PM 2.5 particles may also increase susceptibility to infections [9] . Indeed, this type of pollution can harm human airways, promoting viral infections, and diminish the immune response [7, 10] . The World Health Organization's air quality guidelines recommend that PM 2.5 concentration should not exceed a 10 µg/m 3 annual mean and 25 µg/m 3 24-hour mean [11] . Due to combustion processes, road traffic is the main emission source of atmospheric PM 2.5 [12] . Other sources of PM 2.5 in urban environments include Sahara dust events [13] , shipping [14] , secondary inorganic aerosol or biomass burnin [15] , combustion processes in thermal power stations and other industrial sectors, the transport of anthropogenic aerosols from central Europe to Mediterranean areas, and certain agricultural activities [16] . Spain, which still consumes a considerable amount of fossil fuels, is quite near to the Sahara desert and has approximately 47 million inhabitants. The Mediterranean zone has been identified as a crossroads of air masses with many kinds of aerosols caused by many anthropogenic and natural sources, such as the resuspension of dust from Africa, the production of sea salt, industrial and urban aerosols, fires, and smoke from Eastern Europe [17] . We have recently demonstrated that the Spanish lockdown did not decrease air pollution (NO 2 , CO, SO 2 , and PM 10 ) considering meteorological factors on the pollutants' levels [18] . Furthermore, the O 3 levels were generally increased during this period. In the current paper, we focus on the variations of air pollution in terms of fine particulate matter, wich could be associated with the COVID-19 spread [7] , in several representative Spanish cities during the lockdown using PM 2.5 pollution and COVID-19 data. Meteorological parameters, which significantly affect air pollution [19, 20] have also been taken into account. Several studies in the same research line have shown evidence of significant reductions of severe PM 2.5 pollution during COVID-19 lockdowns in other countries, such as India [21] and Malaysia [22] . However, an increase of 20.5% for PM 2.5 during first month of SARS-CoV-2 outbreak was reported in Tehran [23] . In China, which probably applied one of the strictest lockdown measures, the fine particulate matter in the atmosphere was not significantly reduced either during the COVID-19 lockdown [24] . On the other hand, some studies have reported positive association between COVID-19 daily new cases and PM 2.5 levels in the Netherlands [25] , in Milan [26] and in China [27] . However, additional research is needed to test the exploratory associations found between PM 2.5 pollution and COVID-19 prevalence so far [28] . In this paper, we investigate the impact of environmental data and PM 2.5 level on the COVID-19 outbreak, modelling PM 2.5 level and COVID-19 spread.

Seven major Spanish cities were considered in this study (see Fig. 1 ). Table 1 shows the information of each selected city with populations (on January 1, 2019) that vary from 97,260 to 3,266,126 inhabitants [29]. PM 2.5 pollution data was obtained from official web pages: Bilbao [30] ; Madrid [31] ; San Sebastián [30] ; Santiago [32] ; Valladolid [33]; Vigo [32] and Vitoria [30] . This pollution data was obtained from the traffic station of each city (see Table 1 ) whose levels were mainly determined by traffic emissions [34] . Every day, PM 2.5 (in µg/m 3 ) were collected from each station from March 4 to April 14, 2019, and from March 2 to April 12, 2020, using the sampling method defined by the current Directive 2015/1480 [35] instead of the previous [36], following the gravimetric method of determining PM 2.5 mass fraction in suspended particulate matter [37] . The measurements are commonly performed with active samplers operating at 2.3 m 3 /h over a sampling period of 24 h. The range of application of this European Standard is from 1 µg/m 3 (detection method limit) up to approximately Table 2 provides a city-level statistical summary of the PM 2.5 levels in each of the periods considered for the analysis, where the normal period includes both the period comprised from March 4 to April 14, 2019, and the period from March 2 to March 14, 2020, just before the minor lockdown started.

The Application Programming Interface of the OpenData platform of the State Meteorological Agency was used to download the meteorological data necessary for this analysis. Eight meteorological stations were selected for the meteorological variables considered in this study: temperature, precipitation, wind velocity, min and max atmospheric pressure, and sunlight time, that is, number of hours with a solar irradiance over 120 W/m 2 . The minimum pressure was discarded for the analysis because of the high correlation (very close to 1) between maximum and minimum pressure values. Table 3 provides a statistical summary of the meteorological variables considered corresponding to the entire period under study (including the normal, minor, and major lockdown periods).

COVID-19 data was also downloaded from multiple local and regional webpages: Bilbao [38] , Madrid [39], San Sebastián [38] , Santiago de Compostela [40] , Valladolid [41] , Vigo [40] , and Vitoria [38] . Data was collected for the period comprised from March 18 to April 12, 2020, and corresponded to the number of accumulated COVID-19 cases in each city.

The R programming language [42] was used for the statistical analysis with several R packages: effects [43] , ggplot2 [44] , INLA [45, 46] , lubridate [47] , RCurl [48] , sjPlot [49] and XML [50] .

In order to discriminate between the effects of meteorology and lockdown, the meteorological variables (temperature, precipitation, wind velocity, sunlight time, or atmospheric pressure), which modify pollutant levels [51] , were considered in the statistical model. Weekend days were also considered because of their reduced pollutant levels as a result of less road traffic. Thus, the daily PM 2.5 pollutant levels of the seven cities were fitted through a statistical model considering meteorological variables, weekend days, and lockdown periods of time. The PM 2.5 pollutant levels for a city i on date t were modeled by Eq. (1), including quadratic terms capable of capturing non-linear relationships between the meteorological variables and the PM 2.5 level.

In this regression model, α is the global intercept of the model, β kj (k = 1, 2, 3, 4, 5 and j = 1, 2) quantifies the corresponding meteorological covariate effect (in quadratic form, or not) on the (PM 2.5 ) it values, γ quantifies weekend days effect on the (PM 2.5 ) it values, ρ i represents the cityspecific effect at no lockdown PM 2.5 levels, δ 1 and δ 2 indicate the overall lockdown effect (minor and major, repectively) on (PM 2.5 ) it levels, and δ 1i and δ 2i indicate the city-specific effect (minor and major, repectively) on the (PM 2.5 ) it level. Bank holidays were computed as weekend days. Therefore, this model estimates the variations of PM 2.5 pollution levels because of lockdown while simultaneously accounting for week-day and meteorological effects.

The association between COVID-19 spread and the PM 2.5 levels and environment was also studied for all the cities it + Weekend t + i City i + ( 1 + 1i )Minor l ockdown t + 2 + 2i Major l ockdown t included in the study. Hence, the number of accumulated COVID-19 cases in each city was modelled in terms of each of the environmental covariates available (temperature, rain, wind velocity, sunlight time, maximum atmospheric pressure, and PM 2.5 level) through a Poisson model. Thus, the accumulated COVID-19 cases on date t, y t , was modelled as follows:

where α is the global intercept of the model, x t represents the environmental covariate, β refers to the coefficient that measures the magnitude of the effect of x t on log(µ t ), and δ t is a temporally-structured effect for day t to control for serial correlation, which was defined by a first-order random walk. Lagged covariate effects were considered in the model by replacing the term x t by x t−7 , and x t−14 , which allows assessing the possible effect of the environmental conditions on the previous two weeks on the total number of cases reported on date t. The model described by Eq. (2) was fitted using the Integrated Nested Laplace Approximation (INLA) method [45, 46] .

A stepwise algorithm was applied to the regression model represented by Eq. (1) to find the subset of the variables providing the best model in terms of the Akaike information criterion. The overall variables included in this model and the coefficients associated with each one are shown in Table 4 .

First, it is worth noting that the two precipitation-related variables were discarded, suggesting the absence of an association between precipitation and PM 2.5 values, and also that the maximum pressure and weekend days have a positive association with PM 2.5 levels, while wind velocity has a negative relationship with the particulate matter levels. The model also suggests that temperature and the number of sunlight hours have a quadratic relationship with PM 2.5 levels, which makes interpretation more challenging. Nevertheless, for the range of values attained by temperature and number of sunlight hours during the period under study, the coefficients estimated for the linear and quadratic (2) y t = Po t log t = + x t + t forms of these two variables indicate that higher values of these two variables are associated with higher PM 2.5 levels.

The results obtained mostly agree with other studies in the literature. Thus, regarding the positive association of PM 2.5 with temperature and sunlight hours, dry sunny weather frequently leads to prevent the vertical dispersion of pollutants due to thermal inversion [52] and increases their concentration, generating significant smog episodes. Indeed, sunny weather also favors photochemical reactions [24, 53] while dry conditions prolong aerosols and atmospheric loadings [17] . The increase of wind speed can also decrease PM 2.5 levels [54, 55] , while lower wind speeds can promote a reduction of particulate matter in the air because of an significant increase of deposition [53] . The rise of atmospheric pressure seems to positively affect PM formation [56] . Table 5 shows the coefficients of determination (R 2 ) for each model using PM 2.5 data with Eq. (1) .

From this analysis, it follows that the PM 2.5 levels seem to be quite dependent on meteorological factors while the inclusion of city-level and lockdown effects is fundamental to improve R 2 values.

The minor lockdown led to overall increases in PM 2.5 levels in the cities considered (see results of Table 4 ). Nevertheless, the full analysis of the proposed model requires the consideration of the city-specific coefficients omitted in Table 4 . In fact, the global city-specific effects and the global period (no lockdown, minor or major lockdown) effects were employed for the estimation of the city-specific marginal effects of the PM 2.5 levels. The city-specific effects of the city-period interactions were also considered in this analysis. These results show the PM 2.5 variations due to the COVID-19 lockdown in each city. Figure 2 shows these city-specific marginal effects with the 95% confidence intervals estimated with the Eq. (1) for the PM 2.5 pollution levels during minor lockdown, major lockdown, and no lockdown.

The point estimates represented in this figure show the adjusted effect of the combination of city and period under study on PM 2.5 pollution levels. The differences between PM 2.5 concentrations during the normal period, minor, and major lockdown were not significantly different in many of the cities. However, there was a rising trend in the minor lockdown PM 2.5 levels in some cities considering meteorological and week-day effects, especially in Bilbao and San Sebastián. Considering that relative humidity was not included in the linear regression, these results may simply be an artifact due to high relative humidity episodes that can improve the aqueous-phase oxidation of pollutants such as SO 2 and PM 2.5 levels [24] . Both are coastal cities in the north of Spain, where higher humidity is usual and are more likely to receive atmospheric particles from shipping and On the other hand, the major lockdown period led to a general reduction in fine particulate matter, although only one statistically significant reduction was found in Santiago de Compostela, from 12.41±0.60 to 8.90±1.16 µg/m 3 , in terms of marginal effects. This reduction of PM 2.5 particles must be related to the drastically reduced human activity during the Spanish lockdown [24] . However, the relationship between traffic and anthropogenic activity with particulate matter sources and particle size is not necessarily unequivocal [12] , so that the variations shown may not always be as clear as might be expected. Nevertheless, the reduction of this fine particulate matter was not expected to be very high because these inhalable particles are emitted in large volumes and persist in the air for longer periods, which means they can spread easier [57] . In fact, these low reductions are in in good agreement with our previos study of variations in air quality in terms of PM 10 levels during this period [18] . Since a significant part of this fine particulate matter is from natural sources, a reduction in emissions of human origin would need a much longer lockdown to avoid severe PM 2.5 pollution. Figure 3 shows that the strict national lockdown imposed in Spain flattened the epidemic curve as expected by the Spanish authorities.

The relationship between PM 2.5 exposure or meteorological factors (data shown in Figs. S1-S6 in the Supplementary material) and COVID-19 data (Fig. 3 ) during the Spanish lockdown is shown in Fig. 4 .

Thus, no association is observed between COVID-19 accumulated cases and PM 2.5 levels or climate patterns, contrary to what we could expect according to recent results on the effect of particulate matter levels [25] [26] [27] or climate conditions [58, 59] . Anyhow, it is still unclear the relationship between COVID-19 transmission and PM 2.5 exposure which is in need of further investigation and many controversial results have been reported about the effect of climate on the COVID-19 spread, partly as a consequence of the different statistical methodologies chosen [60] . We have used univariate Poisson models accounting for the presence of temporal autocorrelation in the data. The inclusion of such a temporal term allows capturing the epidemic growth and reduces the chances of obtaining spurious or artefactual associations, which can easily arise when correlation coefficients are computed. The fact that many published studies are strongly based on correlation coefficients, together with the existing uncertainty about COVID-19 data, are sufficient reasons for still being cautious about the association between COVID-19 and the environment or PM 2.5 pollution. Furthermore, the use of multivariate modelsthat allow accounting for several of the factors (environmental or non-environmental) that are possibly implicated in COVID-19 is highly advisable, although we discarded the use of these models in this case due to the length of the time series available. Fig. 4 Effects of each environmental covariate and PM 2.5 exposure on COVID-19 spread estimated for each city under study. Each dot represents the estimation of the coefficient (β parameter in Eq. (2)), whereas the segment corresponds to the 95% credible interval associated with the estimation

Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain after one month follow up

The effects of short-term exposure to selected heavy metals carried by airborne fine particles on neural biomarkers during dust storms. Ltd: Hum Ecol Risk Assess

Human health risk assessment due to ambient PM10 and SO2 by an air quality modeling technique

Health benefits of PM10 reduction in Iran

Effect of PM2.5 on daily outpatient visits for respiratory diseases in Lanzhou

Short-term elevation of fine particulate matter air pollution and acute lower respiratory infection

Can exposure to PM2.5 particles increase the incidence of coronavirus disease 2019 (COVID-19)?

Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Generation of avian influenza virus (AIV) contaminated fecal fine particulate matter (PM2.5): Genome and infectivity detection and calculation of immission

Ambient (outdoor) air pollution [Internet]. WHO

Impact of traffic flows and meteorological events on the hourly elemental composition of fine and coarse particles at an urban site

Trends in ambient ozone, nitrogen dioxide, and particulate matter concentrations over the Maltese Islands and the corresponding health impacts. Sci Total Environ

Estimated health impacts from maritime transport in the Mediterranean region and benefits from the use of cleaner fuels

Characterization and spatial source apportionments of ambient PM10 and PM2.5 during the Heating period in Tian ' jin

Analysis of PM10 and PM2.5 concentrations in an urban atmosphere in Northern Spain

Chemical characterization of fine particles (PM2.5) at a coastal site in the South Western Mediterranean during the ChArMex experiment

Changes in air pollution during COVID-19 lockdown in Spain: a multi-city study

Airborne Transmission of SARS-CoV-2

Temperature significant change COVID-19 Transmission in 429 cities. medRxiv

Effect of restricted emissions during COVID-19 on air quality in India

Air quality status during 2020 Malaysia Movement Control Order (MCO) due to 2019 novel coronavirus (2019-nCoV) pandemic

Impact of SARS-CoV-2 on ambient air particulate matter in Tehran

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Air Pollution Exposure and Covid-19

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in

Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China

COVID-19 prevalence and fatality rates in association with air pollution emission concentrations and emission sources

eus -Toda la información, trámites y servicios del Gobierno Vasco

Portal de datos abiertos del Ayuntamiento de Madrid

Calidade do Aire de Galicia

/1480/UE of the Commission of 28th August 2015 amending several annexes to Directives 2004/107/EC and 2008/50/EC laying down the rules concerning the assessment of ambient air quality

Ambient air: standard gravimetric measurement method for the determination of PM10 and PM2.5 mass concentration of suspended particulate matter

Informes con la actualización de datos sobre la evolución del nuevo coronavirus COVID-19

El mapa y la evolución del coronavirus en Galicia

Situación epidemiológica del coronavirus en Castilla y León

R: A language and environment for statistical computing

An R Companion to Applied Regression

Elegant Graphics for Data Analysis

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Bayesian spatial modelling with R-INLA

Dates and Times Made Easy with lubridate

RCurl: General Network (HTTP/ FTP/… Client Interface for R. R package version 1

Data visualization for statistics in social science

XML: Tools for Parsing and Generating XML Within R and S-Plus

COVID-19 lockdowns cause global air pollution declines with implications for public health risk Authors Affiliations: ORCIDs : Key words : Introduction : medRxiv

Meteorological mechanism for a large-scale persistent severe ozone pollution event over eastern China in 2017

Risk evaluation of environmentally persistent free radicals in airborne particulate matter and in fl uence of atmospheric factors

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during

The effect of meteorological conditions on air pollution in Siedlce

Meteorological influences on seasonal variations of air pollutants (SO2, NO2, O3, CO, PM2.5 and PM10) in the Dhaka Megacity

International trade linked with disease burden from airborne particulate pollution

Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming. medRxiv

Influence of temperature and relative humidity on the occurrence of COVID-19 pandemic: An observational study in 57 countries. medRxiv

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

The authors would like to acknowledge the University of Valencia for the Talent attraction VLC-CAMPUS PhD fellowship (conceded to C.B-S) and the Fundación UCV San Vicente Mártir for the economic support.

Mártir through the Grants 2019-231-003UCV and 2020-231-001UCV (conceded to Á.S-A).

Conflict of interest Authors have no conflict of interests.

Air pollution was analyzed at the city-level through a regression model to determine the changes of PM 2.5 during the Spanish lockdown while accounting for the effect of some meteorological factors. While the 4-week national lockdown reduced the COVID-19 accumulated cases in Spain, the results of this study show a significant decrease of PM 2.5 pollution in only one city. Furthermore, no relationship between COVID-19 spread and PM 2.5 exposure or weather patterns was found during this period in Spain.

The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s40201-022-00786-2.