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The coronavirus pandemic has infected more than 100 million people worldwide with COVID-19, with millions of deaths across the globe. In this research, we explored the effects of environmental and weather variables with daily COVID-19 cases and COVID-19 fatalities in Istanbul, Turkey. Turkey has the 8th highest number of COVID-19 cases globally, with the highest infections and deaths in Istanbul. This may be the first study to conduct a comprehensive investigation for environmental quality (air quality pollutants, e.g., PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, etc.), weather parameters (temperature, humidity) and COVID-19 in Turkey. The authors collected meteorological data from 11 March 2020 to 8 February 2021 and COVID-19 data from Istanbul and other regions. The results from empirical estimations, correlation analysis, and quantile on quantile techniques support that air quality and temperature significantly influence COVID-19 deaths in Istanbul. This research may help policymakers and health scientists to take specific measures to reduce the spread of coronavirus across different global cities.The effects of air quality on COVID-19 in Istanbul was investigated.The study applied correlation and quantile on quantile techniques over daily data.Temperature significantly induces the spread of COVID-19 in Istanbul at all quantiles.Air quality and Nitrogen are positively linked with COVID-19 new cases.
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Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran were carried out during the lockdown periods (February-April) in 2020 and 2021. We evaluated the differences in temporal emissions of six air pollutants (NO2, CO, SO2, O3, PM2.5, and PM10) at various time scales, including diurnal, monthly, and relative changes. The results of ground-based measurements indicated that for all pollutants except O3, the magnitude decreased in 2020 (11-42%) compared to the baseline period (2015-2021). As a result of eased restrictions and unfavorable meteorological conditions, the reduction in air pollutants was lower in 2021 (5-32%), and PM2.5 and PM10 levels increased (3.75 and 11.22%). Satellite-based concentrations (NO2, CO, SO2, and AOD) varied from −8 to 54% in 2020 and from −41 to 60% in 2021 compared to 2019 as the pre-lockdown year. Concerning AOD, the trend is consistent with dust events during March and April in our region. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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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.
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Highlights: Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran were carried out during the lockdown periods (February-April) in 2020 and 2021. We evaluated the differences in temporal emissions of six air pollutants (NO2, CO, SO2, O3, PM2.5, and PM10) at various time scales, including diurnal, monthly, and relative changes. The results of ground-based measurements indicated that for all pollutants except O3, the magnitude decreased in 2020 (11-42%) when compared to the baseline period (2015-2021). As a result of eased restrictions and unfavorable meteorological conditions, the reduction in air pollutants was lower in 2021 (5-32%), and PM2.5 and PM10 levels increased (3.75 and 11.22%). Satellite-based concentrations (NO2, CO, SO2, and AOD) varied from -8 to 54% in 2020 and from -41 to 60% in 2021 compared to 2019 as the pre-lockdown year. Concerning AOD, the trend is consistent with dust events during March and April in the region of interest. Air quality improved in 2020 by lockdown restrictions and in 2021 degraded by eased restrictions and unfavorable meteorological conditions. The diurnal patterns of the air pollutants during lockdown periods remained the same as in the pre-lockdown period with a lower magnitude. Dust events affected the concentration of PM2.5 and PM10 in March and April. Columnar remotely sensed air pollutants, could be used for monitoring air quality in Tehran, especially in terms of NO2 and CO. Air quality improved in 2020 by lockdown restrictions and in 2021 degraded by eased restrictions and unfavorable meteorological conditions.The diurnal patterns of the air pollutants during lockdown periods remained the same as in the pre-lockdown period with a lower magnitude.Dust events affected the concentration of PM2.5 and PM10 in March and April.Columnar remotely sensed air pollutants, could be used for monitoring air quality in Tehran, especially in terms of NO2 and CO. [ FROM AUTHOR]
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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)
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Shanghai, one of China's most important economic centres, imposed a citywide lockdown in April and May 2022 to contain a resurgence in cases of the coronavirus disease in 2019. Compared with the 2020 lockdown, the 2022 lockdown occurred in a warm season and lasted much longer, thereby serving as a relevant real-world test of the response of ambient ozone (O3) concentrations to emission reductions in a high-O3 season. In this study, we analysed surface observations of O3 and nitrogen dioxide (NO2) concentrations and satellite-retrieved tropospheric NO2 and formaldehyde (HCHO) column concentrations in the first 5 months of 2022 with comparisons to the year 2021. During the 2-month 2022 lockdown, the maximum daily 8 h average (MDA8) O3 concentrations at 1 or more of the city's 19 sites exceeded China's air quality standard of 160 µgm-3 21 times, with the highest value being 200 µgm-3. The city-average MDA8 O3 concentration increased by 13 % in April–May 2022 year-on-year, despite sharp declines in NO2 surface and column concentrations (both by 49 %) and a 19 % decrease in the HCHO column concentration. These results show that the reductions in O3 precursors and other pollutants during the 2022 lockdown did not prevent ground-level O3 pollution. An analysis of meteorological data indicates that there were only small changes in the meteorological conditions, and there was little transport of O3 from the high-O3 inland regions during the 2022 lockdown, neither of which can account for the increased and high concentrations of O3 that were observed during this period. The mean HCHO/NO2 ratio in April–May increased from 1.11 in 2021 to 1.68 in 2022, and the correlation between surface O3 and NO2 concentrations changed from negative in 2021 to positive in 2022. These results indicate that the high O3 concentrations in 2022 were mainly due to large reductions in the emissions of NOx and that the decrease in the concentrations of volatile organic compounds (VOCs) could not overcome the NO titration effect. During the 2022 lockdown, Shanghai's urban centre remained VOC-sensitive despite drastic reductions in road transportation (73 %–85 %) and industrial activities (∼60 %), whereas its semi-rural areas transitioned from VOC-limited to VOC–NOx-co-limited regimes. Our findings suggest that future emission reductions similar to those that occurred during the lockdown, such as those that will result from electrifying transportation, will not be sufficient to eliminate O3 pollution in urban areas of Shanghai and possibly other VOC-limited metropoles without the imposition of additional VOC controls or more substantial decreases in NOx emissions.
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Background: With the popularization of the Internet and medical knowledge, more and more people are learning about allergic rhinitis (AR) on the Internet. Objective: This study aims to analyze the epidemiological characteristics and online public attention to AR in Wuhan, China, utilizing the most popular search engine in mainland China and meteorological data of Wuhan. Methods: To study the Internet attention and epidemiological characteristics of AR in Wuhan, the search volume (SV) of "Allergic Rhinitis" in Mandarin and AR-related search terms from 1 January 2014 through 31 December 2021 were recorded. For user interest, the search and demand data were collected and analyzed. Results: The yearly average Baidu SV of AR in both Wuhan and China increased year by year but began to decline gradually after the COVID-19 pandemic. Baidu SV of AR in Wuhan exhibited significant seasonal variation, with the first peak was from March to May and the second peak occurring between September and October. Correlation analysis revealed a moderate positive correlation between the monthly average SV of "Allergic Rhinitis" and "Mites" and "Mites + Pollen Allergy" in Wuhan, a weak positive correlation between the monthly average SV of "Allergic Rhinitis" and "Pollen Allergy," and a positive correlation between monthly SV of "Allergic Rhinitis" and the meteorological index of pollen allergy (MIPA). Conclusion: The attention given to the topic on the internet, as measured by the search volume, was reflective of the situation in Wuhan, China. It has the potential to predict the epidemiological characteristics of AR and help medical professionals more effectively plan seasonal AR health education.
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COVID-19 , Rhinitis, Allergic, Seasonal , Rhinitis, Allergic , Rhinitis , Humans , Rhinitis, Allergic, Seasonal/epidemiology , Pandemics , Infodemiology , COVID-19/epidemiology , Rhinitis, Allergic/epidemiology , China/epidemiologyABSTRACT
A prediction method for estimation of sales based on Prophet with a consideration of nonlinear events and conditions by a modified Optuna is proposed. Linear prediction does not work for a long-term sales prediction because purchasing actions are based on essentially nonlinear customers' behavior. One of nonlinear prediction methods is the well-known Prophet. It, however, is still difficult to adjust the nonlinear parameters in the Prophet. To adjust the parameters, the Optuna is widely used. It, however, is not good enough for parameter tuning by the Optuna. Therefore, the Optuna is modified with a short-term moving mean and standard deviation of the sales for final prediction. More than that, specific event such as typhoon event is to be considered in the sales prediction. Through experiments with a real sales data, it is found the sensitivity of the parameters the upper window, lower window, event dates, etc. for the final sales and the effect of the Optuna is 11.73% Also, it is found that the effect of the consideration of Covid-19 is about 2.4% meanwhile the effect of the proposed modified Optuna is around 3 % improvement of the prediction accuracy (from 80 % to 83 %).
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The purpose of this study was to investigate and assess how restrictive COVID-19 precautions affect air quality in Zonguldak, as well as to determine the relationship between air quality and meteorological variables under these conditions. Daily PM2.5, PM10, SO2, and NOx concentrations and meteorological data, from 1 March to 31 May 2018, 2019, and 2020 were collected for this research. During the 2020 restrictive COVID-19 precautions, it was determined that concentrations of air pollutants were different and low based on the 95% confidence interval by using paired t-test samples. Meteorological variables were found to be similar to previous years, and the correlation between them and air pollutants was found to be significant (P < 0.01) but low according to Pearson correlations. As a result, meteorological variables were determined to have no direct effect on the low concentrations of air quality emissions during the 2020 pandemic. The overall findings revealed that anthropogenic impact has a negative impact on air quality and the air quality had improved during the COVID pandemic. Furthermore, the restriction on the region's coalmines during the COVID-19 pandemic has significant impact on the improvement of air quality.
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Impact-based forecasts and warnings (IBFs) are seen as important drivers for adequate anticipation and assessment of potential threats to public safety as they give a better understanding of the weather event's impacts. To prepare for impacts of weather events and prevent weather-related accidents, road maintenance services are actively using weather information in their daily work routine. This paper looks into the requirements that road maintenance services have for IBFs and how weather forecasts are used at the moment. The study is part of an interdisciplinary research project and follows a qualitative social science research approach. Findings show that the following factors are general user requirements: relevance of information, recognition of spatial and temporal requests, acceptability, comprehensibility, and technical demands. These are also applicable to IBFs with the extension to provide a benefit for road maintenance services in situations that rarely occur and where no embodied knowledge in the organization is existent.
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To date, research regarding the changes of the sulfur and nitrogen rates in Wuhan during the summer is limited. In this study, we analyzed the air quality in Wuhan, China, using water-soluble ion, gaseous precursor, and weather data. A Spearman correlation analysis was then performed to investigate the temporal changes in air quality characteristics and their driving factors to provide a reference for air pollution control in Wuhan. The results indicate that SO2 in the atmosphere at Wuhan undergoes secondary conversion and photo-oxidation, and the conversion degree of SO2 is higher than that of NO2. During the summers of 2016 and 2017, secondary inorganic atmospheric pollution was more severe than during other years. The fewest oxidation days occurred in summer 2020 (11 days), followed by the summers of 2017 and 2014 (25 and 27 days, respectively). During the study period, ion neutralization was the strongest in summer 2015 and the weakest in August 2020. The aerosols in Wuhan were mostly acidic and NH4+ was an important neutralizing component. The neutralization factors of all cations showed little change in 2015. K+, Mg2+, and Ca2+ level changes were the highest in 2017 and 2020. At low temperature, high humidity, and low wind speed conditions, SO2 and NO2 were more easily converted into SO42− and NO3−.
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This study investigated the impacts of the COVID-19 pandemic on the electricity consumption of a university dormitory building in the southern US. The historical electricity consumption data of this university dormitory building and weather data of an on-campus weather station, which were collected from January 1st, 2017 to July 31st, 2020, were used for analysis. Four inverse data-driven prediction models, i.e, Artificial Neural Network, Long Short-Term Memory Recurrent Neural Network, eXtreme Gradient Boosting, and Light Gradient Boosting Machine, were exploited to account for the influence of the weather conditions. The results suggested that the total electricity consumption of the objective building decreased by nearly 41% (about 276,000 kWh (942 MMBtu)) compared with the prediction value during the campus shutdown due to the COVID-19. Besides, the daily load ratio (DTR) varied significantly as well. In general, the DTR decreased gradually from 80% to nearly 40% in the second half of March 2020, maintained on a relatively stable level between 30% to 60% in April, May, and June 2020, and then slowly recovered to 80% of the normal capacity in July 2020.
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[...]it is of the utmost imperative to have accurate solar energy prediction using long-term meteorological data. The results indicated 42% less charging time and energy storage up to 63% higher than that of the reference case of straight longitudinal fins with the same PCM mass [3]. Because of PCMs’ latent heat storage capacity, they also have wide applications in the solar field [4,5]. [...]efficient buildings that possess combined benefits for energy, the economy, and the environment are essential.
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This paper investigated the influence and interactions of air pollution concentrations by using the stochastic boosted regression trees between variables for each station and the impact of the COVID-19 Movement Control Order at Ipoh City air quality station. The one-hour data were gathered from the Department of Environment from January until June 2019 and 2020. Two thousand two hundred thirty-one data of particles, gases (Nitrogen oxides, Sulphur Dioxide, Ozone, Carbon Monoxide) concentrations and meteorological data (wind speed, wind directions, temperature, and relative humidity) were captured. The BRT model development process with an algorithm using a comprehensive package, R Software and its packages to understand the variability and trends. It was found that the relationship between the number of samples and number of trees (nt) of 4372 for oob were found the best iterations obtained. The performance of the boosting model was assessed and found that the FAC2 was 0.91, the R2 values were above 0.56 (R = 0.74), and the Index of Agreements (IOA) was 0.67, which fall ranges are within an acceptable for model performance. The Relative Variable Importance (RVI) that influenced PM2.5 for non-MCO data was CO (18.9% ), SO2 (14.6 %), O3 (12.9 %), and wd (10.66 %) while CO (22.6%), RH (13.4%), 14.7% and O3 (12.1%) were RVI factors influenced to PM2.5 concentrations during MCO periods. Estimating the strength of interaction effects (SIE) between variables was 0.24 for CO-wind directions, followed by 0.19 for ozone-wind speeds and 0.15 for NO2-CO. Results showed that the model developed was within the acceptable range and could be used to understand particles and identify important parameters that influence particle concentrations.
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The stringent control measures in China to curb the spread of Coronavirus disease (COVID-19) have had profound societal and environmental impacts, including changes in energy consumption practices and thereby in air pollutant emissions. In this study, a suite of satellite and numerically assimilated air pollution and meteorological data combined with information on energy consumption practices and nighttime light (NTL) was used to evaluate the effects of these COVID-19 control measures on air quality. These data revealed that control measures reduced aerosols mostly over central and eastern parts of China by countering favorable meteorological conditions for increased aerosols. The control measures reduced short-lived nitrogen dioxide (NO2) with little influence on long-lived carbon monoxide (CO). Consistent with energy production and energy consumption statistics in different sectors, NTL data suggest that high human mobility within the residential sector and reduced activity in other sectors during the implementation of control measures explain small but significant decreases in black carbon and sulfate aerosols, respectively, during this period. Overall, these results provide useful information for policy makers and the scientific community by clarifying the contributions of meteorological factors and energy consumption to changes in air quality. This information can guide the development of air pollution mitigation strategies and provides insight into the air pollution status in China and the potential for long-distance transport.
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Using statistical models, the average hourly ozone (O3) concentration was predicted from seven meteorological variables (Pearson correlation coefficient, R = 0.87–0.90), with solar radiation and temperature being the most important predictors. This can serve to predict O3 for cities with real time meteorological data but no pollutant sensing capability. Incorporating other pollutants (PM2.5, SO2, and CO) into the models did not significantly improve O3 prediction (R = 0.91–0.94). Predictions were also made for PM2.5, but results could not reflect its peaks and outliers resulting from local sources. Here we make a comparative analysis of three different statistical predictor models: (1) Multiple Linear Regression (MLR), (2) Support Vector Regression (SVR), and (3) Artificial Neuronal Networks (ANNs) to forecast hourly O3 and PM2.5 concentrations in a mid-sized Andean city (Manizales, Colombia). The study also analyzes the effect of using different sets of predictor variables: (1) Spearman coefficients higher than ± 0.3, (2) variables with loadings higher than ± 0.3 from a principal component analysis (PCA), (3) only meteorological variables, and (4) all available variables. In terms of the O3 forecast, the best model was obtained using ANNs with all the available variables as predictors. The methodology could serve other researchers for implementing statistical forecasting models in their regions with limited pollutant information.
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With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.
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COVID-19 , Pandemics , Forecasting , Humans , Japan/epidemiology , Machine Learning , SARS-CoV-2ABSTRACT
In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.