Abstract
The emergence of COVID-19 and the spread of this novel disease around the world in 2020 has entailed several cultural changes; some of those changes are positive for the environment, such as the decrease in the concentration of atmospheric particulate matter. We compared the concentrations of PM2.5 and PM10 recorded in October and November 2019 (pre-pandemic period) with the concentrations recorded from May to October 2020 (pandemic period) in the city of Arequipa, Peru. A significant decrease in the concentration of PM2.5 (less than 21.0%) and PM10 (less than 21.5%) was observed on Sundays, when population movement was strongly restricted. First, we observed a significant correlation between PM2.5 and PM10 concentration in the atmosphere and the number of infections reported in Arequipa, Peru. However, when we removed the data of Sundays from the database, these correlations were no longer significant. Subsequently, we correlated PM2.5 and PM10 concentrations with the number of COVID-19 infections on the same day and up to a 20-day delay and found that from day 15 to day 18, PM10 concentration was significantly correlated with COVID-19 infections, suggesting that SARS-CoV-2 might circulate attached to the coarse particle (PM10) and that this fraction would act as infection vector. However, these results may reflect other factors, such as social or economic factors that could explain the dynamics of infection in Arequipa, Peru. Further research is needed to better understand the dynamics of the SARS-CoV-2 pandemic.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-021-13408-5.
Keywords: COVID-19, SARS-CoV-2, Lockdown, PM2.5, PM10, Arequipa, Peru
Introduction
In January 2020, a new virus, named SARS-CoV-2, was identified; later, a global public health emergency was declared by the World Health Organization (WHO 2020). This newly identified virus (SARS-CoV-2) causes an acute respiratory illness called coronavirus disease 2019 (COVID-19) (Gautam and Hens 2020), which can be mistaken for pneumonia (Jiang et al. 2020) and can spread rapidly in humans with close contact to infected persons (Bherwani et al. 2020). Many countries adopted local- or national-level measures to prevent the spread of the virus, resulting in restriction of transport, trade, and industrial activities (Stratoulias and Nuthammachot 2020). On the other hand, this decrease in human activities produced a positive impact on the environment, with air quality having been improved in several regions of the world (Muhammad et al. 2020; Wang and Su 2020; Gautam 2020).
In March 2020, the Government of Peru declared a state of national emergency and implemented lockdown of activities to tackle theCOVID-19 outbreak (Arias Velásquez and Mejía Lara 2020); this situation continued for a few months, with some activities having been eased. In the urban area of the city of Arequipa, Peru, a recent study recorded values of PM2.5 (72 ± 23 μg m−3) and PM10 (116 ± 41 μg m−3) above the maximum limits established in the country (Larrea Valdivia et al. 2020). Our objectives were (I) to quantify the concentrations of particulate matter (PM2.5 and PM10) in an urban area of the city of Arequipa and observe the effect of the restriction measures implemented by the government and (II) to explore a possible relationship between the amount of PM2.5 and PM10 in the atmosphere and the number of COVID-19 infections recorded in Arequipa, Peru.
Materials methods
Study area
This study was conducted in the city of Arequipa, Peru, which is located on the slopes of the Andes in the southern region of the country (Fig. 1). This city is surrounded by mountains, with an average altitude of 2335 m a.s.l. and a desert climate, with an average annual rainfall of about 100 mm. The selected sampling site was located in the downtown area of the city of Arequipa (−16° 24′ 28″ S latitude, −71° 32′ 16″ W longitude), characterized by heavy traffic; indeed, 64 of the 135 companies of the urban transport system operating in the city run across this area.
Fig. 1.
Study site in the Arequipa downtown region, Peru
Levels of PM2.5 and PM10 and study period
In the study area, the PM2.5 and PM10 concentration was recorded using Dustmate Particle Collector (Turnkey Instruments, Northwich, UK) at the height of 2.5 m from the ground. The Dustmate Particle Collector is a photometric sampler that uses the technology of scattered light to detect the concentration of dust and inhalable particles with diameter within the range of 0.4 to 20 μm. A built-in sampling pump draws in air with 600 mL min−1 flow, and a continuous airflow containing particles passes through a laser beam in the test chamber (Turnkey Instruments 2016). The Dustmate recorded PM1, PM2.5, PM10, and PST concentration data every 30 min from 7:00 am to 7:00 am the next day; however, we only used the PM2.5 and PM10 concentrations, since we previously calibrated the data obtained with Dusmate Particle against values obtained with high volume sampler (HI-vol 3000, Ecotech) and observed significant correlations between both sampling methods, with values of R2=0.74 for PM2.5 and R2=0.81 for PM10.
Study period
During the study, data were collected for PM2.5 and PM10 between September 9 and October 24, 2019 (pre-pandemic period), when activities in the city of Arequipa were normal; thus, data were obtained for a total of 45 days. In 2020, data were collected during the COVID-19 pandemic period, from May 16 to October 25 (pandemic period); thus, a total of 151 days of PM2.5 and PM10 data were obtained. In Peru, social isolation was implemented for 15 days, beginning on March 16, 2020, when all activities in the country were interrupted, except for the essential ones (security, health, food transportation). Later, some activities were authorized, such as public and private transportation. When we started collecting data, on May 16, there was a total lockdown only on Sundays, which lasted until Sunday, September 20, when public and private transportation was allowed in the streets of Arequipa.
Meteorological parameters
Daily temperature values (average, maximum, and minimum), relative humidity, and average and maximum wind speed were provided by the weather station located at the Alfredo Rodríguez Ballón Airport. These data were used to explore possible relationships between the meteorological parameters and the PM2.5 and PM10 concentrations. Precipitation was not included in the analysis because no precipitations occurred during the study period.
Statistical analyses
An analysis of variance (ANOVA) was used to compare (I) mean concentrations of PM2.5 and PM10 between study years, (II) mean concentrations of PM2.5 and PM10 between different weekdays in each study year, and (III) number of COVID-19 infections among weekdays. Values were statistically significant at p < 0.05. The assumptions of normal distribution and homoscedasticity were checked before analysis. When heteroscedasticity was found, it was modeled to be incorporated into the ANOVA. Means were compared using a post hoc LSD Fisher test. The Pearson correlation coefficient was used to study the relationship between (I) PM2.5 and PM10 with the meteorological variables (II) PM2.5 and PM10 with the number of COVID-19 infections. All analyses were performed using InfoStat software coupled to R (Di Rienzo et al. 2018). All data used in this research are presented in the supplementary material (Data.xlsx).
Results and discussion
Concentration of PM2.5 and PM10
Particulate matter (PM2.5 and PM10) concentration data collected in September and October 2019 (pre-pandemic period) and in 2020 (pandemic period) in Arequipa, Peru, are presented in Table 1. The PM2.5 and PM10 values recorded in this study are comparable to the PM2.5 concentrations of 72 ± 23 μg m−3 and PM10 concentrations of 116 ± 41 μg m−3 reported in a previous study conducted in downtown Arequipa 2018 (Larrea Valdivia et al. 2020).
Table 1.
Concentration of PM2.5 and PM10 in Arequipa, Peru, during September and October 2019 (pre-COVID-19 pandemic period) and from May to October 2020 (COVID-19 pandemic period)
| September and October 2019 (pre-COVID-19 pandemic period) | From May to October 2020 (COVID-19 pandemic period) | ANOVA (p value) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | Mean | Max. | Min | n | Mean | Max. | Min | |||
| PM2.5 (μg m−3) | Full data set | 45 | 52.15 | 82.87 | 26.90 | 151 | 48.43 | 94.41 | 18.36 | 0.1361 |
| Monday | 7 | 49.07 | 58.78 | 38.00 | 21 | 48.99a | 67.57 | 21.09 | ||
| Tuesday | 7 | 51.79 | 66.90 | 45.12 | 20 | 51.86a | 86.99 | 28.51 | ||
| Wednesday | 6 | 53.91 | 82.87 | 38.44 | 23 | 50.94a | 92.38 | 27.79 | ||
| Thursday | 7 | 51.80 | 59.70 | 46.48 | 20 | 49.38a | 65.71 | 35.44 | ||
| Friday | 6 | 59.74 | 68.57 | 42.20 | 23 | 49.18a | 68.43 | 29.32 | ||
| Saturday | 6 | 51.46 | 72.49 | 38.75 | 23 | 53.35a | 94.41 | 31.85 | ||
| Sunday | 6 | 47.93 | 71.77 | 26.90 | 21 | 37.65b | 68.71 | 18.36 | ||
| ANOVA | (p value) | 0.5899 | 0.0047 | |||||||
| PM10 (μg m−3) | Full data set | 45 | 79.10 | 125.45 | 45.43 | 151 | 75.36 | 154.92 | 26.66 | 0.2771 |
| Monday | 7 | 76.40 | 87.72 | 58.21 | 21 | 72.79a | 101.22 | 39.50 | ||
| Tuesday | 7 | 79.87 | 106.25 | 69.00 | 20 | 81.69a | 154.92 | 41.90 | ||
| Wednesday | 6 | 85.20 | 125.45 | 62.57 | 23 | 78.88a | 129.35 | 39.46 | ||
| Thursday | 7 | 79.12 | 95.52 | 64.68 | 20 | 77.88a | 103.11 | 55.43 | ||
| Friday | 6 | 87.78 | 100.77 | 62.97 | 23 | 77.75a | 107.89 | 46.32 | ||
| Saturday | 6 | 75.37 | 111.21 | 57.96 | 23 | 82.24a | 138.34 | 48.16 | ||
| Sunday | 6 | 70.23 | 105.89 | 45.43 | 21 | 55.46b | 99.97 | 26.66 | ||
| ANOVA | (p value) | 0.5435 | 0.0003 | |||||||
ANOVA between years and ANOVA among days of the week; different letters indicate statistical difference
The average concentrations of PM2.5 and PM10 recorded in the pandemic period are lower than those observed in the pre-pandemic period (Table 1), but without significant differences. Recent studies reported a decrease in atmospheric particulate matter as a result of the lockdown implemented in the different countries; for example, in the city of Milan, Italy, a reduction in PM2.5 and PM10 levels was observed during the pandemic period (Zoran et al. 2020); in the southeast of the UK, a decrease in particulate matter can be attributed to lockdown restrictions (Wyche et al. 2021); and in the city of Baghdad, Iraq, PM2.5 and PM10 concentrations were reduced by 8% and 15%, respectively, during the first partial and total lockdown (Hashim et al. 2021).
The average PM2.5 and PM10 concentrations recorded in 2020 (pandemic period) were significantly lower on Sundays than on the other days of the week (Table 1). This result may be attributed mainly to the restrictions that were implemented in Arequipa on Sundays during complete lockdown. Lower PM2.5 and PM10 concentrations were also observed on Sundays in 2019 than in the other days of the week, but without significant differences from PM2.5 and PM10 concentrations recorded on the other days of the week. Average PM2.5 and PM10 values obtained on Sundays during COVID-19 pandemic period were 21.0% and 21.5% lower, respectively, than in the pre-pandemic period (2019). This decrease in particulate matter concentrations is similar to that reported for Hat Yai city, Thailand, where lower concentrations of PM2.5 (21.8%) and PM10 (22.9%) were recorded during the pandemic period, as a result of the reduced economic and commercial activity (Stratoulias and Nuthammachot 2020). However, the lockdown effect was not as significant as reported in the Yangtze River Delta region, where a reduction of 26–48% for atmospheric PM2.5 and 29–34% for atmospheric PM10 was reported (Lee et al. 2020).
Effect of meteorological parameters on PM2.5 and PM10
The relationship between meteorological variables and concentrations of atmospheric particulate matter for 2019 showed a positive correlation between maximum temperature and PM10 concentration, and a negative correlation between the average wind speed and PM2.5 and PM10 levels (Table 2). Plocoste et al. (2020) studied the relationship between PM10 concentration and air temperature in the Caribbean for 11 years and concluded that the positive correlation can be attributed to a greenhouse effect caused by the presence of dust clouds in the atmosphere, as may have occurred in the city of Arequipa. On the other hand, the negative correlation between wind speed and particulate matter concentration was previously reported for the city of Arequipa, in a previous study showing the negative correlation between PM10 and average wind speed. In that work, the authors explain that an average increase in wind speed decreases the concentration of particles in the atmosphere due to a washing effect (Larrea Valdivia et al. 2020).
Table 2.
Pearson correlation coefficients between meteorological parameters and concentrations of PM2.5 and PM10 in the atmosphere of Arequipa, Peru, during 2019 and 2020
|
September and October 2019 (pre-COVID-19 pandemic period) |
||||||
| PM2.5 | PM10 | |||||
| n | Pearson R | p value | n | Pearson R | p value | |
| T (mean) | 45 | 0.13 | 0.4002 | 45 | 0.30 | 0.0430 |
| T (max) | 45 | 0.15 | 0.3245 | 45 | 0.39 | 0.0077 |
| T (min) | 45 | 0.08 | 0.5905 | 45 | 0.14 | 0.3727 |
| WS (mean) | 45 | −0.46 | 0.0015 | 45 | −0.32 | 0.0316 |
| WS (max) | 45 | −0.04 | 0.8148 | 45 | −0.06 | 0.6931 |
| RH (mean) | 45 | 0.11 | 0.4897 | 45 | −0.20 | 0.1836 |
|
From May to October 2020 (COVID-19 pandemic period) |
||||||
| PM2.5 | PM10 | |||||
| n | Pearson R | p value | n | Pearson R | p value | |
| T (mean) | 125 | 0.06 | 0.5338 | 125 | 0.07 | 0.4137 |
| T (max) | 125 | 0.03 | 0.7707 | 125 | 0.04 | 0.6217 |
| T (min) | 125 | 0.05 | 0.6096 | 125 | 0.01 | 0.9109 |
| WS (mean) | 125 | −0.09 | 0.3450 | 125 | −0.03 | 0.7746 |
| WS (max) | 125 | 0.01 | 0.8945 | 125 | 0.07 | 0.4195 |
| RH (mean) | 125 | −0.13 | 0.1633 | 125 | −0.27 | 0.0020 |
T temperature, WS wind speed, RH relative humidity
During the pandemic period, only a negative correlation was observed between the percentage of ambient relative humidity and PM10 concentration. These changes in correlation patterns could be explained by restrictions imposed during the pandemic period, such as traffic restrictions, which caused changes in the levels of particulate matter in this period, especially on Sundays.
Particulate matter (PM2.5 and PM10) and its relationship with the number of COVID-19 infections in the city of Arequipa, Peru
The average number of COVID-19 infections during the study for each day of the week is shown in Fig. 2. The number of infections recorded on Sundays was significantly lower than on other days of the week; this result is due to reduced health service on Sundays (hospitals, clinics, sanatoriums, laboratories), with fewer number of tests for COVID-19 being made on those days.
Fig. 2.
Number of COVID infections (± Standard Error) on different days of the week, from May to October 2020 in Arequipa, Peru. ANOVA among days of the week; different letters indicate statistical difference
First, we performed a Pearson correlation analysis between COVID-19 transmission data and PM2.5 and PM10 concentrations recorded during 2020 (May to October) and obtained a value of R2=0.21 (p=0.0093) for PM2.5 and R2=0.18 (p=0.0255) for PM10. These results might lead to the conclusion that an increase in the concentration of particulate matter would cause an increase in of COVID-19 cases; indeed, the virus would be transported by PM10, which would act as a vector of the pathogen, as suggested by Bontempi (2020a). However, this author was not able to prove a direct relationship between PM10 concentration and COVID-19 transmission in the Lombardy region (Italy). Nevertheless, recent studies suggest that under stable climatic conditions and high concentrations of particles in the atmosphere, SARS-Cov-2 could create clusters with PM (Bontempi 2020a; Domingo and Rovira 2020; Srivastava 2020), although according to Yao et al. (2020), there is no evidence that the virus is airborne. However, recent studies indicate that the pandemic spread patterns are usually caused by a multiplicity of factors, such as environmental, economic, and social ones. Therefore, neglecting this multiplicity of factors in the analysis may lead to erroneous conclusions, and an interdisciplinary and multidimensional approach is necessary to understand the geographical diversity of infections (Bontempi 2020b; Bontempi et al. 2020).
It is important to note that our results are influenced by the values recorded on Sundays, when the concentrations of particulate matter were reduced due to traffic restrictions; on the other hand, fewer COVID-19 infections are reported on Sundays. Therefore, we removed the data corresponding to Sundays from the data set and then performed the Pearson correlation analysis again; the results showed no correlation between PM2.5 and PM10 concentration and COVID-19 infections (Table 3).
Table 3.
Pearson’s correlation analysis between PM2.5 and PM10 concentration and number of COVID-19 infections in the city of Arequipa, Peru. Values corresponding to Sundays were removed from this analysis
| Days of delay | R2 value | p value | Days of delay | R2 value | p value | ||
|---|---|---|---|---|---|---|---|
| PM2.5 | 0 | 0.10 | 0.2679 | PM10 | 0 | 0.04 | 0.6893 |
| 1 | 0.08 | 0.3558 | 1 | 0.04 | 0.3558 | ||
| 2 | 0.06 | 0.4695 | 2 | 0.02 | 0.8427 | ||
| 3 | 0.07 | 0.4423 | 3 | 0.03 | 0.7502 | ||
| 4 | 0.04 | 0.6216 | 4 | 0.01 | 0.8762 | ||
| 5 | 0.06 | 0.4762 | 5 | 0.04 | 0.6645 | ||
| 6 | 0.08 | 0.3527 | 6 | 0.06 | 0.5231 | ||
| 7 | 0.09 | 0.3343 | 7 | 0.06 | 0.4800 | ||
| 8 | 0.11 | 0.2263 | 8 | 0.09 | 0.3329 | ||
| 9 | 0.11 | 0.2065 | 9 | 0.09 | 0.2839 | ||
| 10 | 0.12 | 0.1731 | 10 | 0.13 | 0.1571 | ||
| 11 | 0.03 | 0.6989 | 11 | 0.07 | 0.4345 | ||
| 12 | 0.11 | 0.2118 | 12 | 0.17 | 0.0572 | ||
| 13 | 0.07 | 0.4505 | 13 | 0.13 | 0.1495 | ||
| 14 | 0.08 | 0.3937 | 14 | 0.16 | 0.0685 | ||
| 15 | 0.09 | 0.2966 | 15 | 0.18 | 0.0398 | ||
| 16 | 0.09 | 0.3468 | 16 | 0.19 | 0.0380 | ||
| 17 | 0.10 | 0.2852 | 17 | 0.21 | 0.0205 | ||
| 18 | 0.07 | 0.4785 | 18 | 0.18 | 0.0449 | ||
| 19 | 0.06 | 0.5172 | 19 | 0.17 | 0.0606 | ||
| 20 | 0.06 | 0.5501 | 20 | 0.19 | 0.0435 |
Recent studies reported that between 2 and 14 days can elapse from the day of SARS-CoV-2 infection and the appearance of the first disease symptoms (incubation period) (Kouidere et al. 2020; Lee et al. 2020; Huang et al. 2020). Thus, if SARS-CoV-2 were transported in particulate material, an increase of infection cases should be observed after contamination events. Therefore, we performed the Pearson’s correlation analysis between particulate matter concentrations (PM2.5 and PM10) and COVID-19 infections obtained on subsequent days; the R2 values of Pearson’s correlation, with different days of delay, from day 0 (without delay) to a delay of 20 days, are presented in Table 3. The PM2.5 levels detected in Arequipa were not related to the COVID-19 infections recorded on the same day, or to the infections recorded on the 20 subsequent days; therefore, PM2.5 particle matter fraction would not be a SARS-CoV-2 pathway.
On the other hand, PM10 concentration levels were significantly correlated with the number of COVID-19 infections recorded between 15 days and 18 days later. This result suggests that the SARS-CoV-2 virus could be transported attached to PM10 particles, as suggested by Bontempi (2020a), and that an episode of increased atmospheric particulate matter might increase the number of infections, with this effect being observed 15 days later. In addition, the PM10 concentration is significantly correlated with the number of infections recorded in Arequipa from day 15 to day 18 of the delay, with the highest R2 value being observed on day 17 of delay (R2=0.21). However, a recent study (Bianco et al. 2020) suggests that sunlight would be sufficient to inactivate SARS-CoV-2, which would make transmission in open sites unlikely, which is in disagreement with our results. In addition, Chirizzi et al. (2021) studied the presence of SARS-CoV-2 genetic material (using both real time RT-PCR and Droplet Digital PCR) in particulate matter samples collected from the north and south of Italy (Venice and Lecce, respectively) and observed that the amount of virus copies in PM10 was < 0.8 copies m−3; the authors concluded that virus spread is unlikely in open areas. However, the mean PM10 concentrations reported for Venice (PM10= 17.2 ± 5.2 μg m−3) and Lecce (PM10= 27.0 ± 14.8 μg m−3) are much lower than those recorded in this study; these differences suggest that in Arequipa, Peru, the number of copies per virus would be higher than that reported for Italy, since the concentration of particulate matter is higher.
The correlations between the number of infections and PM10 concentration could be reflecting other factors, such as an increase in population movement and commercial activities, which would increase atmospheric particulate matter; in turn, increasing movement and commercial activities would lead to more people coming into contact, which would ultimately lead to a greater number of infections. However, more studies are needed in different regions under different population, climatic and economic conditions, to understand the dynamics of the SARS-CoV-2 pandemic.
Conclusions
The restrictions implemented by the government between May and October 2020 produced a significant decrease in the levels of PM2.5 and PM10 on Sundays in the city of Arequipa, Peru.
Average temperature was positively correlated with PM10 concentration; the negative correlation between wind speed and PM10 concentration suggests that wind would cause a washing of particles into the atmosphere.
On the other hand, of the two fractions of particulate matter studied (PM2.5 and PM10), only the coarse fraction (PM10) present in the atmosphere could be a vehicle for the transmission of SARS-CoV-2. Indeed, we observed a positive correlation between PM10 concentration and the number of people infected with COVID-19, with a delay of 15 days. However, these results may be influenced by other social or economic factors that could explain the dynamics of infection in Arequipa, Peru. More studies are needed in different regions of the world in order to better understand the dynamics of the SARS-CoV-2 pandemic.
Supplementary Information
(XLSX 265 kb)
Author contribution
EDW: data analysis, discussion of the results, and writing of the manuscript.
ALV: methodology planning, discussion of the results, and administration of funds.
JRL: discussion of the results, acquisition of funds, and administration of funds.
JSP: data collection of PM2.5 and PM10 in 2019 and 2020.
CVH: support in data collection of PM2.5 and PM10 in 2019 and 2020.
Funding
The authors acknowledge the funding provided by UNSA-INVESTIGA of the National University of San Agustín de Arequipa (IBA-040) to carry out this research.
Data availability
Not applicable.
Declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- Arias Velásquez RM, Mejía Lara JV. Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima. Urban Clim. 2020;33:100664. doi: 10.1016/j.uclim.2020.100664. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bherwani H, Gupta A, Anjum S, Anshul A, Kumar R (2020) Exploring dependence of COVID-19 on environmental factors and spread prediction in India. Res Square. 10.21203/rs.3.rs-25644/v1
- Bianco A, Biasin M, Pareschi G, Cavalieri A, Cavatorta C, Fenizia C, Galli P, Lessio L, Lualdi M, Radealli E, Saulle I, Trabattoni D, Zanutta A, Clerici M (2020) UV-C irradiation is highly effective in inactivating and inhibiting SARS-CoV-2 replication. MedRxiv preprint. 10.1101/2020.06.05.20123463
- Bontempi E. First data analysis about possible COVID-19 virus airborne diffusion due to air particulate matter (PM): the case of Lombardy (Italy) Environ Res. 2020;186:109639. doi: 10.1016/j.envres.2020.109639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bontempi E. Commercial exchanges instead of air pollution as possible origin of COVID-19 initial diffusion phase in Italy: More efforts are necessary to address interdisciplinary research. Environ Res. 2020;188:109775. doi: 10.1016/j.envres.2020.109775. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bontempi E, Vergalli S, Squazzoni F. Understanding COVID-19 diffusion requires an interdisciplinary, multi-dimensional approach. Environ Res. 2020;188:109814. doi: 10.1016/j.envres.2020.109814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chirizzi D, Conte M, Feltracco M, Dinoi A, Gregoris E, Barbaro E, La Bella G, Ciccarese G, La Salandra G, Gambaro A, Contini D. SARS-CoV-2 concentrations and virus-laden aerosol size distributions in outdoor air in north and south of Italy. Environ Int. 2021;146:106255. doi: 10.1016/j.envint.2020.106255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Di Rienzo JA, Casanoves F, Balzarini MG, Gonzalez L, Tablada M, Robledo CW (2018) InfoStat versión 2018. Centro de Transferencia InfoStat, FCA, Universidad Nacional de Córdoba, Argentina. http://www.infostat.com.ar. Accessed 19 Mar 2021
- Domingo JL, Rovira J. Effects of air pollutants on the transmission and severity of respiratory viral infections. Environ Res. 2020;187:109650. doi: 10.1016/j.envres.2020.109650. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gautam S. COVID-19: air pollution remains low as people stay at home. Air Qual Atmos Health. 2020;13:853–857. doi: 10.1007/s11869-020-00842-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gautam S, Hens L. COVID-19: impact by and on the environment, health and economy. Environ Dev Sustain. 2020;22:3867–3869. doi: 10.1007/s10668-020-00818-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hashim BM, Al-Naseri SK, Al-Maliki A, Al-Ansari N. Impact of COVID-19 lockdown on NO2, O3, PM2.5 and PM10 concentrations and assessing air quality changes in Baghdad, Iraq. Sci Total Environ. 2021;754:141978. doi: 10.1016/j.scitotenv.2020.141978. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang L, Zhang X, Zhang X, Wei Z, Zhang L, Xu J, Liang P, Xu Y, Zhang C, Xue A. Rapid asymptomatic transmission of COVID-19 during the incubation period demonstrating strong infectivity in a cluster of youngsters aged 16-23 years outside Wuhan and characteristics of young patients with COVID-19: A prospective contact-tracing study. J Infect. 2020;80(6):e1–e13. doi: 10.1016/j.jinf.2020.03.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jiang F, Deng L, Zhang L, Cai Y, Cheung CW, Xia Z. Review of the clinical characteristics of coronavirus disease 2019 (COVID-19) J Gen Intern Med. 2020;35(5):1545–1549. doi: 10.1007/s11606-020-05762-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kouidere A, Kada D, Balatif O, Rachik M, Naim M (2020) Optimal control approach of a mathematical modeling with multiple delays of the negative impact of delays in applying preventive precautions against the spread of the COVID-19 pandemic with a case study of Brazil and Cost-effectiveness. Chaos, Solitons Fractals. In press. 10.1016/j.chaos.2020.110438 [DOI] [PMC free article] [PubMed]
- Larrea Valdivia AE, Reyes Larico JA, Salcedo Peña J, Wannaz ED. Health risk assessment of polycyclic aromatic hydrocarbons (PAHs) adsorbed in PM2.5 and PM10 in a region of Arequipa, Peru. Environ Sci Pollut Res. 2020;27:3065–3075. doi: 10.1007/s11356-019-07185-5. [DOI] [PubMed] [Google Scholar]
- Lee H, Kim K, Choi K, Hong S, Ryu S, Son H. Incubation period of the coronavirus disease 2019 (COVID-19) in Busan, South Korea. J Infect Chemother. 2020;26(9):1011–1013. doi: 10.1016/j.jiac.2020.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Muhammad S, Long X, Salman M. COVID-19 pandemic and environmental pollution: a blessing in disguise? Sci Total Environ. 2020;728:138820. doi: 10.1016/j.scitotenv.2020.138820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Plocoste T, Calif R, Euphrasie-Clotilde L, Brute F. Investigation of local correlations between particulate matter (PM10) and air temperature in the Caribbean basin using Ensemble Empirical Mode Decomposition. Atmos Pollut Res. 2020;11:1692–1704. doi: 10.1016/j.apr.2020.06.031. [DOI] [Google Scholar]
- Srivastava A. COVID-19 and air pollution and meteorology-an intricate relationship: a review. Chemophere. 2020;263:128297. doi: 10.1016/j.chemosphere.2020.128297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stratoulias D, Nuthammachot N. Air quality development during the COVID-19 pandemic over a medium-sized urban area in Thailand. Sci Total Envirom. 2020;746:141320. doi: 10.1016/j.scitotenv.2020.141320. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Turnkey Instruments Ltd (2016) Dustmate: operating instructions. https://turnkey-instruments.com/wp-content/uploads/2016/11/DustMate-Operating-Instructions.pdf. Accessed 19 March 2021
- Wang Q, Su M (2020) A preliminary assessment of the impact of COVID-19 on environment – a case study of China. Sci Total Environ 728:138915 [DOI] [PMC free article] [PubMed]
- WHO (World Health Organization), (2020). Coronavirus disease 2019 (COVID-19) situation report-36, February 25, 2020. World Health Organization Geneva. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200225-sitrep-36-covid-19.pdf Accessed on 20 November 2020.
- Wyche KP, Nichols M, Parfitt H, Beckett P, Gregg DJ, Smallbone KL, Monks PS. Changes in ambient air quality and atmospheric composition and reactivity in the South East of the UK as a result of the COVID-19 lockdown. Sci Total Environ. 2021;755:142526. doi: 10.1016/j.scitotenv.2020.142526. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yao M, Zhang LMJ, Zhou L. On airborne transmission and control of SARSCov-2. Sci Total Environ. 2020;731:139178. doi: 10.1016/j.scitotenv.2020.139178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zoran MA, Savastru RS, Savastru DM, Tautan MN (2020) Assessing the relationship between surface levels of PM2. 5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Sci Total Environ:738, 139825. 10.1016/j.scitotenv.2020.139825 [DOI] [PMC free article] [PubMed]
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