The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Quantifying the impact of meteorological factors and green infrastructure location on particulate matter (PM) mitigation in North Macedonia using sensor collected data

Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this contribution is to analyse the influence of the green zones on air quality mitigation through sensor measurements, and to identify the correlation with the meteorological factors. Actually, the objective focuses on identifying the most significant correlation between PM2.5 and PM10 concentrations and the wind speed, as well as a negative correlation between the PM concentrations and wind speed across different measurement locations. Additionally, the estimation of slight correlation between the PM concentrations and the real feel temperature is detected, while insignificant correlations are found between the PM concentrations and the actual temperature, pressure, and humidity. In this paper the effect of the pandemic restriction rules COVID-19 lockdowns and the period without restriction are investigated. The sensor data collected before the pandemic (summer months in 2018), during the global pandemic (summer months 2020), and after the period with restriction measures (2022) are analysed.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study. / Influencia de los factores meteorológicos en la gravedad de la COVID-19 en España: estudio observacional ecológico.

OBJECTIVES: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. METHODS: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the CarlosIII Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). RESULTS: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7±2.6% vs. 11.5±2.6%; P=9.9×10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0±0.6% vs. 3.1±0.8%; P=1.7×10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; P=3.0×10-6) and mortality (Rho: -0.70; P=5.3×10-9). In those provinces with a mean air temperature <10°C mortality by COVID-19 was twice that of those with >16°C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non-standardized ß coeff.: -0.24; 95%CI: -0.31 to -0.16; P=2.38×10-8). CONCLUSIONS: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Influence of Meteorological Factors on Covid-19 Incidence in the Conditions of Ukrain

Objective: The coronavirus disease (COVID-19) is a problem for the health care systems of many countries around the world. Seasonal nature of influenza and other the respiratory viral diseases is commonly known. The nature of the relationship between the frequency of registration of cases of COVID-19 and natural factors is still being studied by researchers. The purpose is to determine the influence of air temperature, relative humidity, wind speed, and atmospheric pressure on the incidence of the coronavirus disease COVID-19 in the conditions of Ukraine. Materials and methods. Official reports of the Ministry of Health of Ukraine and data from daily monitoring of meteorological indicators conducted by the Sumy Regional Hydrometeorology Center were used in the paper. Descriptive and analytical ways of epidemiological method of investigation were applied. The search for parameters of interrelation between the frequency of registration of COVID-19 cases and meteorological cases took place using of program "Statistica", namely the relevant tools of this program: "Analysis"/ "Multiple regression". Results and Discussion: In the period under study from March 25, 2020 to December 31, 2021 in Sumy Oblast of Ukraine, three waves of rise in the incidence were registered. In the third wave of rise in the incidence, in autumn 2021 the frequency of registration of COVID-19 cases reached 1684.9 per 100 thousand of people, despite the fact that almost 70 % of the population had already recovered or were vaccinated. Meteorological factors in the conditions of Ukraine have little influence on the rate of spread of COVID-19. The value of multiple correlation coefficients was within those limits, which are considered moderate in terms of influence. A moderate inverse correlation was established between the frequency of registration of COVID-19 cases and indicators of air temperature, and a direct correlations-with indicators of relative air humidity. Conclusion(s): In the conditions of Ukraine, the studied meteorological factors (air temperature, relative humidity, wind speed, atmospheric pressure) indirectly influenced the intensity of the epidemic process of COVID-19. the strength of this influence was either weak or moderate.Copyright © 2023, Ibn Sina Trust. All rights reserved.

An effect of temperature on the spread of COVID-19 in Qatar, Kuwait and other gulf countries. (Special issue on COVID.)

Background: COVID-19 has emerged as a serious pandemic that emerged during since the end of 2019. The dissemination and survival of coronaviruses have been demonstrated to be affected by ambient temperature in epidemiological and laboratory research. The goal of this investigation was to see if temperature plays a role in the infection produced by this novel coronavirus. Methods: Between March 29, 2020, and September 29, 2020, daily confirmed cases and meteoro-logical parameters in many Gulf countries were collected. Using a generalized additive model, we investigated the nonlinear relationship between mean temperature and COVID-19 confirmed cases.. To further investigate the association, we employed a piecewise linear regression. Results: According to the exposure-response curves, the association between mean temperature and COVID-19 cases was nearly linear in the window of 21 - 30C while it is almost flat beyond that window. When the number was below 21C (lag 0-14), each 1C increase was associated with a 4.861 percent (95 percent CI: 3.209 - 6.513) increase in mean temperature (lag 0-14). Our sensitiv-ity analysis confirmed these conclusions. Conclusions: Our findings show a positive linear association between mean temperature and the number of COVID-19 cases with a threshold of 21C. There is little evidence that COVID-19 case numbers would rise as the weather becomes colder, which has important consequences for making health strategy and decision.

The Effect of Meteorological Factors on the COVID-19 Pandemic in Northeast Turkiye.

Introduction Although various studies have been conducted on the relationship between meteorological factors and coronavirus disease 2019 (COVID-19), this issue has not been sufficiently clarified. In particular, there are a limited number of studies on the course of COVID-19 in the warmer-humidity seasons. Methods Patients presenting to the emergency departments of health institutions and to clinics set aside for cases of suspected COVID-19 in the province of Rize between 1 June and 31 August 2021 and who met the case definition based on the Turkish COVID-19 epidemiological guideline were included in this retrospective study. The effect of meteorological factors on case numbers throughout the study was investigated. Results During the study period, 80,490 tests were performed on patients presenting to emergency departments and clinics dedicated to patients with suspected COVID-19. The total case number was 16,270, with a median daily number of 64 (range 43-328). The total number of deaths was 103, with a median daily figure of 1.00 (range 0.00-1.25). According to the Poisson distribution analysis, it is found that the number of cases tended to increase at temperatures between 20.8 and 27.2°C. Conclusion It is predicted that the number of COVID-19 cases will not decrease with the increase in temperature in temperate regions with high rainfall. Therefore, unlike influenza, there may not be seasonal variation in the prevalence of COVID-19. The requisite measures should be adopted in health systems and hospitals to manage increases in case numbers associated with changes in meteorological factors.

SEASONALITY OF METEOROLOGICAL FACTORS INFLUENCING THE COVID-19 ERA IN COASTAL AND INLAND REGIONS OF BANGLADESH

We aim to explore the seasonal influences of meteorological factors on COVID-19 era over two distinct locations in Bangladesh using a generalized linear model (GLM) and wavelet analysis. GLM model findings show that summer humidity drives COVID-19 transmission to coastal and inland locations. During the summer in the coastal area, a 1 °C earth's skin temperature increase causes a 41.9% increase in COVID (95% CL 86.32%-2.54%) transmission compared to inland. Relative humidity was recorded as the highest at 73.97% (95% CL, 99.3%, and 48.63%) for the coastal region, while wind speed and precipitation reduced confirmed cases by -38.62% and -22.15%, respectively. Wavelet analysis showed that coastal meteorological parameters were more coherent with COVID-19 than inland ones. The outcomes of this study are consistent with subtropical climate regions. Seasonality and climatic similarity should address to estimate COVID-19 trends. High societal concern and strong public health measures may decrease meteorological effect on COVID-19. [ FROM AUTHOR] Copyright of Geocarto International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study.

Objectives: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. Methods: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). Results: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%; p = 9.9 × 10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%; p = 1.7 × 10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; p = 3.0 × 10-6) and mortality (Rho: -0.70; p = 5.3 × 10-9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non standardised B coeff.: -0.24; IC 95%: -0.31 to -0.16; p = 2.38 × 10-8). Conclusions: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Objetivos: Evaluar si factores meteorológicos y geográficos pudieron relacionarse con la gravedad de la COVID-19 en España. Métodos: Estudio ecológico, a escala provincial, que analiza la influencia de factores meteorológicos y geográficos en la hospitalización y mortalidad por COVID-19 en las 52 provincias españolas (24 costeras y 28 del interior), durante las tres primeras olas. Los datos de hospitalizaciones y mortalidad se obtuvieron del Instituto de Salud Carlos III (ISCIII). Los datos epidemiológicos del Instituto Nacional Estadística (INE) y la Red Nacional de Vigilancia Epidemiológica (RENAVE). Las variables meteorológicas de la Agencia estatal de meteorología (AEMET). Resultados: El porcentaje de pacientes hospitalizados por COVID-19, del total de personas infectadas, fue inferior en las provincias costeras que en las del interior peninsular (8,7 ± 2,6% vs. 11,5 ± 2,6%; p = 9,9 × 10−5). De igual manera la costa registró menor porcentaje de mortalidad que el interior peninsular (2,0 ± 0,6% vs. 3,1 ± 0,8%; p = 1,7 × 10−5). La temperatura media correlacionó negativamente con la hospitalización (Rho: −0,59; p = 3,0 × 10−6) y la mortalidad por COVID-19 (Rho: −0,70; p = 5,3 × 10−9). Las provincias con una temperatura media <10 °C duplicaron la mortalidad por COVID respecto a las de >16 °C. La mortalidad se relacionó con la localización provincial (costa/interior), la altitud, la edad de la población y la temperatura media, siendo esta última la variable asociada de manera independiente (Coef. B no estandarizado: −0,24; IC 95%: −0,31 a −0,16; p = 2,38 × 10−8). Conclusiones: La mortalidad por COVID-19 durante las tres primeras olas de la pandemia en nuestro país se asoció inversamente con la temperatura media.

The Effects of Air Pollution and Meteorological Factors in the Transmission and Lethality of COVID-19

The COVID-19 epidemic continues to have a negative impact on the economy and public health. There is a correlation between certain limits (meteorological factors and air pollution statistics) and verified fatal instances of Corona Virus Disease 2019 (COVID-19), according to several researchers. It has not yet been determined how these elements affect COVID-19. Using air pollution data and meteorological data from 15 cities in India from 2020 to 2022, Convergent Cross Mapping (CCM) is utilized to set up the causal link with new confirmed and fatal cases of COVID-19 in this study. Our experimental results show that the causal order of the factors influencing the diagnosis of COVID-19 is: humidity, PM25, temperature, CO, NO2, O3, PM10. In contrast to other parameters, temperature, PM25, and humidity are more causally associated with COVID-19, while data on air pollution are less causally related to the number of new COVID-19 cases. The causal order of the factors affecting the new death toll is as follows: temperature, PM25, humidity, O3, CO, PM10, NO2. The causality of temperature with new COVID-19 fatalities in India was higher than the causation of humidity with new COVID-19 deaths, and O3 also showed higher causality with it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China.

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh.

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave.

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China.

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021.

We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m3. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.

The relationship among air pollution, meteorological factors and COVID-19 in the Brussels Capital Region.

In great metropoles, there is a need for a better understanding of the spread of COVID-19 in an outdoor context with environmental parameters. Many studies on this topic have been carried out worldwide. However, there is conflicting evidence regarding the influence of environmental variables on the transmission, hospitalizations and deaths from COVID-19, even though there are plausible scientific explanations that support this, especially air quality and meteorological factors. Different urban contexts, methodological approaches and even the limitations of ecological studies are some possible explanations for this issue. That is why methodological experimentations in different regions of the world are important so that scientific knowledge can advance in this aspect. This research analyses the relationship between air pollution, meteorological factors and COVID-19 in the Brussels Capital Region. We use a data mining approach that is capable of extracting patterns in large databases with diverse taxonomies. Data on air pollution, meteorological, and epidemiological variables were processed in time series for the multivariate analysis and the classification based on association. The environmental variables associated with COVID-19-related deaths, cases and hospitalization were PM2.5, O3, NO2, black carbon, radiation, air pressure, wind speed, dew point, temperature and precipitation. These environmental variables combined with epidemiological factors were able to predict intervals of hospitalization, cases and deaths from COVID-19. These findings confirm the influence of meteorological and air quality variables in the Brussels region on deaths and cases of COVID-19 and can guide public policies and provide useful insights for high-level governmental decision-making concerning COVID-19. However, it is necessary to consider intrinsic elements of this study that may have influenced our results, such as the use of air quality aggregated data, ecological fallacy, focus on acute effects in the time-series study, the underreporting of COVID-19, and the lack of behavioral factors.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19.

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications.

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.