Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi

The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.

Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry

Volatile organic compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). In this respect, long-term datasets of in situ atmospheric measurements are crucial for characterizing the variability of atmospheric chemical composition, its sources, and trends. The ongoing establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementation of the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOx), and aerosols have been carried out since 2012 at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory, located in the Paris region, France. Within the last decade, VOC measurements were conducted offline at SIRTA, until the implementation of real-time monitoring which started in January 2020 using a proton-transfer-reaction quadrupole mass spectrometer (PTR-Q-MS).The dataset acquired during the first 2 years of online VOC measurements provides insights into their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOx, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtained results. Results also include insights into VOC main sources and the influence of meteorological conditions and air mass origin on their levels in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably saw a quasi-total lockdown in France in spring and a lighter one in autumn. Therefore, the focus is placed on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. A comparison with gas chromatography data from the Paris city centre consolidates the regional representativity of the SIRTA station for benzene, while differences are observed for shorter-lived compounds with a notable impact of their local sources. This dataset could be further used as input for atmospheric models and can be found at 10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health

Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of 'lockdown') have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 pollutant measurements derived from satellite imagery (MODIS instruments on Terra and Aqua, and TROPOMI on Sentinel-5P) with census data archived by the Organization for Economic Co-operation and Development (OECD). A 91-day timeframe centred on the initial lockdown date of 23 January 2020 was investigated. To perform the projections, OECD data on five variables from 1990 to 2019 (mean population exposure to ambient PM2.5, premature deaths, welfare costs, gross domestic product and population) were used as training data to run the Autoregressive Integrated Moving Average (ARIMA) and multiple regression models. The analysis of the satellite imagery revealed that across the regions of Beijing, Hebei, Shandong, Henan, Xi'an, Shanghai and Hubei, the average concentrations of PM2.5 decreased by 6.2, 30.7, 14.1, 20.7, 29.3, 5.5 and 17.3%, while the NO2 decreased by 45.5, 54.7, 60.5, 58.7, 63.6, 50.5 and 66.5%, respectively, during the period of lockdown restrictions in 2020, as compared with the equivalent period in 2019. Such improvements in air quality were found to be beneficial, reducing in 2020 both the number of premature deaths by approximately 97,390 and welfare costs by over USD 74 billion.

Substantial Changes in Selected Volatile Organic Compounds (VOCs) and Associations with Health Risk Assessments in Industrial Areas during the COVID-19 Pandemic.

During the COVID-19 pandemic, governments in many countries worldwide, including India, imposed several restriction measures, including lockdowns, to prevent the spread of the infection. COVID-19 lockdowns led to a reduction in gaseous and particulate pollutants in ambient air. In the present study, we investigated the substantial changes in selected volatile organic compounds (VOCs) after the outbreak of the coronavirus pandemic and associations with health risk assessments in industrial areas. VOC data from 1 January 2019 to 31 December 2021 were collected from the Central Pollution Control Board (CPCB) website, to identify percentage changes in VOC levels before, during, and after COVID-19. The mean TVOC levels at all monitoring stations were 47.22 ± 30.15, 37.19 ± 37.19, and 32.81 ± 32.81 µg/m3 for 2019, 2020, and 2021, respectively. As a result, the TVOC levels gradually declined in consecutive years due to the pandemic in India. The mean TVOC levels at all monitoring stations declined from 9 to 61% during the pandemic period as compared with the pre-pandemic period. In the current study, the T/B ratio values ranged from 2.16 (PG) to 26.38 (NL), which indicated that the major pollutant contributors were traffic and non-traffic sources during the pre-pandemic period. The present findings indicated that TVOC levels had positive but low correlations with SR, BP, RF, and WD, with correlation coefficients (r) of 0.034, 0.118, 0.012, and 0.007, respectively, whereas negative correlations were observed with AT and WS, with correlation coefficients (r) of -0.168 and -0.150, respectively. The lifetime cancer risk (LCR) value for benzene was reported to be higher in children, followed by females and males, for the pre-pandemic, pandemic, and post-pandemic periods. A nationwide scale-up of this study's findings might be useful in formulating future air pollution reduction policies associated with a reduction in health risk factors. Furthermore, the present study provides baseline data for future studies on the impacts of anthropogenic activities on the air quality of a region.

TIME SERIES ANALYSIS OF METEOROLOGICAL PARAMETERS AND AIR POLLUTION CONCENTRATIONS IN EMILIA-ROMAGNA, ITALY, DURING COVID-19 INFECTION

During the pandemic, Italy experienced several phases of lockdown with different types of restrictions. Starting on February 23rd 2020, 11 municipalities in northern Italy suspended activities in schools, universities, museums, cultural venues, and all public initiatives. The ordinance announcing the national emergency was released on March 11th, stabilising the first lockdown period for the whole of Italy, which lasted until the second half of May. After a phase of cushioned restrictions during the summer, the so-called 'Second Wave' began forcing anew ordinance on October 13th with more stringent restrictions as the number of infections increased. On November 3rd, the "colour system" was introduced with three risk bands-red, orange and yellow-assigned weekly to the regions based on monitoring indicators. The main objective of the present study is to assess the impact of the meteorological and air quality conditions on COVID-19 cases in the region of Emilia-Romagna in Italy during the lockdown periods. Several pollutant time series from the Copernicus Atmosphere Monitoring Service were joined with meteorological data from the daily gridded land-only observational dataset over Europe and then compared with the total number of infections, hospitalisations and deaths. Data provided by the two monitoring systems were processed through an algorithm and organised by provinces and municipalities in Emilia-Romagna, Italy. The explorative analysis, conducted using both time series and seasonally adjusted time series, shows that pollutants most affected by lockdown phases are CO, NO2, PM10, PM2.5 and SO2. The findings in this study may help further studies better understand the variations 2020 and 2021 and the correlation with COVID-19 variables. © British Crown Copyright (2022)

Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou

Airborne aerosol is believed to be an important pathway for infectious disease transmissions like COVID-19 and influenza. However, the effects of dust event days on influenza have been rarely explored, particularly in arid environments. This study explores the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza in a semi-arid city. A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. The case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza. We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased. The greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.061 (1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found that low humidity and temperature had an interaction with PM to aggravate the risk of influenza. In summary, ambient PM and dust events exposure may increase the risk of influenza, and the risk of influenza increases with the dust events duration. Therefore, more efforts from the government as well as individuals should be strengthened to reduce the effect of PM on influenza, particularly in cold and dry weather.

The relation between SARS-CoV-2 cases in Bahrain and meteorological parameters: 27 months of data

Background: There has been a belief in and strong thought about the existence of a relationship between meteorological parameters and the spread of SARS-CoV-2. Many studies have been conducted but with a short period of investigation, i.e., not more than 4 months. Therefore, the relation between 27 months of SARS-CoV-2 recorded data and meteorological parameters is reported. The aim of this study is to use this vast data to examine whether average temperature, average wind speed, and absolute humidity are clearly correlated with the number of infected SARS-CoV-2 cases in Bahrain or not, which may be applicable to countries that have similar topography.Method: The official recorded data of SARS-CoV-2 cases in Bahrain from the first day that SARS-CoV-2 cases were detected (February 24, 2020) until May 18, 2022, along with 4 meteorological parameters (temperature, wind speed, relative humidity, and absolute humidity) were used. The data were analyzed using SPSS where a p-value less than 0.05 was considered as statistically significant.Result: There is a negative significant correlation between new daily cases of SARS-CoV-2 and temperature, T, and absolute humidity, AH, (r = -0.290, -0.317;p < 0.001, respectively). The results also show a positive significant correlation between daily cases of SARS-CoV-2 and wind speed (V) (r = -0.110;p = 0.002). No correlation was found between daily cases of SARS-CoV-2 and relative humidity (r = -0.028;p = 0.429). An empirical relation is reported, allowing the estimation of SARS-CoV-2 cases in Bahrain as a function of three weather parameters: T, AH, and V.

Impact of Different Air Pollutants (PM10, PM2.5, NO2, and Bacterial Aerosols) on COVID-19 Cases in Gliwice, Southern Poland.

Many studies have shown that air pollution may be closely associated with increased morbidity and mortality due to COVID-19. It has been observed that exposure to air pollution leads to reduced immune response, thereby facilitating viral penetration and replication. In our study, we combined information on confirmed COVID-19 daily new cases (DNCs) in one of the most polluted regions in the European Union (EU) with air-quality monitoring data, including meteorological parameters (temperature, relative humidity, atmospheric pressure, wind speed, and direction) and concentrations of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen oxides (NO and NO2), ozone (O3), and carbon monoxide (CO). Additionally, the relationship between bacterial aerosol (BA) concentration and COVID-19 spread was analyzed. We confirmed a significant positive correlation (p < 0.05) between NO2 concentrations and numbers of confirmed DNCs and observed positive correlations (p < 0.05) between BA concentrations and DNCs, which may point to coronavirus air transmission by surface deposits on bioaerosol particles. In addition, wind direction information was used to show that the highest numbers of DNCs were associated with the dominant wind directions in the region (southern and southwestern parts).

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters

This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing  ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models.

Coronavirus (COVID-19) is a highly contagious virus (SARS-CoV-2) that has caused a global pandemic since January 2020. Scientists around the world are doing extensive research to control this disease. They are working tirelessly to find out the origin and causes of the disease. Several studies and experiments mentioned that there are some meteorological parameters which are highly correlated with COVID-19 transmission. In this work, we studied the effects of 11 meteorological parameters on the transmission of COVID-19 in Bangladesh. We first applied statistical analysis and observed that there is no significant effect of these parameters. Therefore, we proposed a novel technique to analyze the insight effects of these parameters by using a combination of Random Forest, CART, and Lasso feature selection techniques. We observed that 4 parameters are highly influential for COVID-19 where [Formula: see text] and Cloud have positive association whereas WS and AQ have negative impact. Among them, Cloud has the highest positive impact which is 0.063 and WS has the highest negative association which is [Formula: see text]. Moreover, we have validated our performance using DLNM technique. The result of this investigation can be used to develop an alert system that will assist the policymakers to know the characteristics of COVID-19 against meteorological parameters and can impose different policies based on the weather conditions.

The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities.

Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day's number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day's deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.

A concise air quality study on impact of COVID-19 lockdown for two industrial cities of Chhattisgarh

‘Good Outcomes from evil situation’ this phrase perfectly fits into the COVID-19 circumstances as several restrictions on anthropogenic activities provided an improvement in the ambient air quality status globally. The study deals with the consequences of COVID-19 lockdown on ambient air quality for 2 major industrial cities ( Raipur and Bilaspur) in Central Indian state Chhattisgarh moreover a comparison of air quality data was made with non-lockdown year (2019). The AQI and critical parameters (such as PM10, PM2.5, SO2 and NOx) were acquired form online available source and then analysed for the study period (2019 and 2020). Noteworthy reduction in AQI and concentration of pollutants in Raipur was detected whereas there was reduction in Bilaspur but it was less than Raipur. Evident changes in the level of pollutants (NOx and PM) were observed during the study. Meteorological parameters such as temperature and relative humidity were also examined for Raipur. Statistical analysis between data of meteorological parameters and AQI for capital city Raipur was also carried out.

Impact of COVID-19 Pandemic Preventing Measures and Meteorological Conditions on the Atmospheric Air Composition in Moscow in 2020

Changes in the atmospheric composition during different periods of 2020 in Moscow which were associated with the COVID-19 pandemic preventing measures as well as corresponding pollutant emission reduction, are investigated. Surface concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), aerosol fraction (PM10), and meteorological parameters during different periods of 2020 were compared with similar data for the previous five years. The analysis of ground-based measurements, as well as of high-resolution satellite distributions of CO and NO2 indicated that the concentration of major pollutants and its spatial distribution in the Moscow region were significantly affected by both restrictive measures and abnormal meteorological conditions in 2020.

Effect of COVID-19 epidemic-led lockdowns on aerosol black carbon concentration, sources and its radiation effect in northeast India

The COVID-19 epidemic-led lockdown (LD) from March 25 to May 31, 2020, had a different level of impact on air quality in the ecologically sensitive region of northeast India, even though the restriction on main anthropogenic activities was expected to reduce particulate matter concentration. The daily average black carbon concentration measured at 880 nm (BC880) was 1.5–15.6 μg m−3 (mean: 5.75±4.24 μg m−3) during the measurement period. It was 9.29±4.11 μg m−3 during pre-LD (February 12–March 21), 4.70±0.95 μg m−3 during LD1 (March 25–April 14), 3.41±0.56 μg m−3 during LD2 (April 15–May 3), 3.69±1.50 μg m−3 during LD3 (May 4–17), 2.94±0.93 μg m−3 during LD4 (May 18–31), and 6.56±5.35 μg m−3 during the Post-LD (June 6–July 3) of 2020. It decreased up to 68% during the lockdowns. The source apportionment based on an improved method showed a significant improvement in the contribution of BC880 sources. The radiation effect determined by Angstrom Absorption Exponent showed that brown carbon accounted for 25% of the aerosol light absorption at 370 nm during the lockdown period. Relative humidity correlates substantially with BC880, while rainfall, temperature, and solar radiation were negatively correlated. The bivariate analysis showed the dominance of local emissions in the BC880 concentrations.Research highlightsBlack carbon concentration decreased up to 68% during the different phases of lockdown.BC associated with fossil fuel was 51–78%, and biomass burning was 22–49%.The fraction of fossil fuel and biomass burning in whole BC fallen to 0.73 and 0.65 during the lockdowns.Air quality improved by about 47–58% on the 4th and 7th day of lockdown.Brown carbon and meteorological parameters significantly impacted aerosol light absorption in this region.

Geospatial View of Air Pollution and Health Risk Over North Indian Region in COVID-19 Scenario

Air pollutant concentration, air quality index (AQI), and Excess risk (ER%) is assessed during January 2020 to June 2021 and in three scenarios including pre-lockdown, lockdown and post-lockdown based on 47 ground station data (during January 2020 to June 2020) distributed over northern part of India (including Delhi, Haryana, Punjab, part of Uttar Pradesh, and part of Rajasthan) using statistics and geographic information system (GIS) techniques. Daily and monthly variations of air pollutants (During January 2020 to June 2021) over the region showed a systematic pattern with high pollutant level during October and November while low during March, April (in dry period) and July–September (in wet period). In three scenarios viz. pre, during and post-lockdown the average concentration for PM2.5 was 71.1 ± 45 µg/m3, 39 ± 20 µg/m3 and 40 + 17 µg/m3, for PM10 was 139 ± 72 µg/m3, 96 ± 55 µg/m3 and 105 ± 57 µg/m3, for NO2 was 28 ± 21 µg/m3, 17 ± 13 µg/m3 and 18 ± 12 µg/m3, for NH3 was 33 ± 24 µg/m3, 25 ± 18 µg/m3 and 29 ± 22 µg/m3, for CO was 1 ± 0.65 mg/m3, 0.7 ± 0.5 mg/m3, and 0.7 ± 0.5 mg/m3, for O3 was 29 ± 20 µg/m3, 39 ± 23 µg/m3 and 39 ± 22 µg/m3 and for SO2 was 14 ± 11 µg/m3, 14 ± 12 µg/m3 and 12.5 ± 8.9 µg/m3. Significant decrease in mean pollutants concentration, AQI and ER % was observed in lockdown period amid COVID-19. PM2.5, PM10, NO2, NH3 and CO decreased by 46%, 31%, 39%, 24% and 34%, respectively, in lockdown scenario as compared to the pre-lockdown scenario while the O3 get increased. A decrease of 39% in AQI was observed as compared to pre-lockdown scenario;however, the difference was less when compared with post-lockdown scenario. The decrease in total ER% was 60.36% over the study area due to improvement in air quality over the region amid COVID-19 lockdown. The meteorological conditions in 2020 were found consistent with respect to 2019 and very less influence was observed on the concentration of air pollutants (less r2 among the pollutants and meteorological parameters).

Correlation between Air Pollution and the Spread and Development of COVID-19 Related Disease

Potential correlation of exposure to polluted air and the spread and co-development of COVID-19 and severe acute respiratory syndrome, caused by SARS-CoV-2, was examined. The emphasis was given on polluted air in the form of suspended particulate matter or liquid particles in gas or air (so-called dust particles). This study was structured as a systematic literature review of multiple research projects carried out across the globe. Impact of the polluted air particles on the virus spread was examined from the temporal and spatial spread. Furthermore, overall impact of particulate matter and COVID-19 disease on human health human was investigated on a microbiological level. Despite some ambiguity, through systematic literature review effect of the polluted air on the increased spread of various viruses was demonstrated. Longer exposure to contaminated airborne dust particles has a negative effect on the human immune system and in the case of infection with COVID 19, may even overload it. This can lead to serious consequences for human health or even cause death. This review article also provides an insight into a more comprehensive analysis of possible correlation between the spreading the virus (SARS-CoV-2) by means of particulate matter and other meteorological variables (such as air temperature and humidity, weather events and climate).

Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.

Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco.

The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO2, 03, SO2, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O3 had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.