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.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

The impact of meteorological factors on the spread of COVID-19.

Background: Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity. Aim: This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19). Patients and Methods: This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1st Regional Directorate. Results: The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality. Conclusion: Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

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.

THREE-DIMENSIONAL ANALYTICAL SOLUTION OF THE ADVECTION-DIFFUSION EQUATION FOR AIR POLLUTION DISPERSION

We develop a new analytical solution of a three-dimensional atmospheric pollutant dispersion. The main idea is to subdivide vertically the planetary boundary layer into sub-layers, where the wind speed and eddy diffusivity assume average values for each sub-layer. Basically, the model is assessed and validated using data obtained from the Copenhagen diffusion and Prairie Grass experiments. Our findings show that there is a good agreement between the predicted and observed crosswind-integrated concentrations. Moreover, the calculated statistical indices are within the range of acceptable model performance.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

Long-term effective and accurate wind power potential prediction, especially for wind farms, facilitates planning for the sustainable development of renewable energy. Accurate wind speed forecasting enhances wind power generation planning and reduces costs. Wind speed time series has nonlinearity, intermittence, and fluctuation, which makes the prediction difficult. Deep learning techniques can be beneficial when there is no specific structure to data. These techniques can predict wind speed with reasonable accuracy and reliability. In this study, four different algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolu-tional Neural Network (CNN), and CNN-LSTM, for three different long-term horizons (6 months, 1 year, and 5 years) are successfully developed using the direct method. GRU method showed a higher degree of accuracy compared to other methods. In addition, it is confirmed that using a multivariate data set increases the model's accuracy compared to the univariate model. A computational cost analysis is also conducted to compare the proposed algorithms. Finally, the power production capacity of the wind farm at a given location, Zabol city, is calculated for the next five years, which is indispensable for planning, management, and economic analysis. The reasonable conformance between the real data and predicted ones is shown to confirm the capability of the proposed model to use in long-term wind speed forecasting.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future. © 2023 by the authors.

Variation of carbon dioxide mole fraction at a typical urban area in the Yangtze River Delta, China

The observations of atmospheric CO2 mole fraction in urban area in China are relative sparse. Here, we present the first-hand observation of atmospheric CO2 mole fraction from 2016 to 2020 at a city station (Hangzhou, abbreviated as HZ) in the Yangtze River Delta, which is one of the strongest CO2 source regions in China. The CO2 mole fraction at an adjacent World Meteorological Organization / Global Atmospheric Watch (WMO/GAW) programme site (Lin'an, LAN) are also presented and compared. The temporal variations, seasonal variations, and influence of COVID-19 pandemic are analyzed. Our results show that, the variations of CO2 mole fraction in Hangzhou are mainly driven by the local emissions, both atmospheric dilution conditions (i.e., wind speed, visibility) and topography, and the temporal variations are apparently different with the suburb site of LAN, although the distance between the two stations is only 50 km. During the observation period, the CO2 mole fraction at HZ is on average 15.6 +or- 0.2 ppm higher than LAN, with two distinct peaks observed at 9:00 and 17:00-18:00, corresponding to traffic rushing hours. The growth rate of atmospheric CO2 mole fraction is 11.2 +or- 0.1 ppm yr-1 before the COVID-19 pandemic (from 2016 to 2019), which is much higher than the suburb site of LAN, 5.4 +or- 0.1 ppm yr-1. The COVID-19 pandemic has led to a plunge of atmospheric CO2 mole fraction at HZ in 2020, with a value of 15.7 +or- 0.7 ppm, corresponding to 3.5% lower than the year of 2019. But at LAN, the annual average CO2 mole fraction in 2020 is 1.5 +or- 0.5 ppm higher than the previous year, similar to the trend in the northern hemisphere. The different annual CO2 mole fraction growth rate at HZ indicates that the CO2 mole fraction at Hangzhou may be dominated by local anthropogenic emissions, despite the transport of airmass from the north and southwest.

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors

Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates

Natural ventilation can play an important role towards preventing the spread of airborne infections in indoor environments. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. In the current study, we propose and validate an efficient strategy for using computational fluid dynamics to assess natural ventilation flow rates under variable conditions, considering the test case of a single-room home in a dense urban slum. The method characterizes the dimensionless ventilation rate as a function of the dimensionless ventilation Richardson number and the wind direction. First, the high-fidelity large-eddy simulation (LES) predictions are validated against full-scale ventilation rate measurements. Next, simulations with identical Richardson numbers, but varying dimensional wind speeds and temperatures, are compared to verify the proposed similarity relationship. Last, the functional form of the similarity relationship is determined based on 32 LES. Validation of the surrogate model against full-scale measurements demonstrates that the proposed strategy can efficiently inform accurate building-specific similarity relationships for natural ventilation flow rates in complex urban environments.

The Role of Traffic Volume on Sound Pressure Level Reduction before and during COVID-19 Lockdown Measures-A Case Study in Bochum, Germany.

During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from -0.99 dB(A) (CI: -1.45; -0.53) to -0.25 dB(A) (CI: -0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (-0.16 dB(A) (CI: -0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00–8:00 LT) and night (19:00–22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect” was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health. © 2022 Elsevier Ltd

How environmental factors affecting COVID-19 transmission? case study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r = -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19′s spread in KSA. © 2022 The Author(s)

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .

PREDICTION OF THE MAXIMUM WIND SPEED IN INDOOR ENVIRONMENTS FOR EFFICIENT NATURAL VENTILATION

The efficient natural ventilation in indoor environments is extremely important especially this period with the appearance of new hazardous viruses such as COVID-19. It is well known that the maximum wind speed causes the lowest individual exposure to hazardous substances in an environment (either indoor or outdoor) and as a result its reliable prediction by a numerical model (either simple or complex) becomes of utmost importance. In this study a deterministic model, that was developed for the outdoor environment, is examined as a possible candidate to predict the maximum wind speed in indoor environments. For the needs of the study a wind tunnel experiment is simulated by the LES methodology in order to acquire the maximum wind speed at various locations in an indoor environment. Then the deterministic model, without any change in its parameters, is validated successfully with the LES maximum wind speeds. The present deterministic model can be incorporated in simple methodologies (e.g. RANS) provided that the latest are able to predict the mean speed, the turbulent intensity and a hydrodynamic time scale. © British Crown Copyright (2022)

Experimental investigation of the effect of surgical masks on outdoor thermal comfort in Xiamen, China.

The COVID-19 pandemic has significantly changed people's lifestyles, and wearing surgical masks in outdoor public spaces has become commonplace. However, few studies have explored the impact of wearing masks on outdoor thermal comfort in different seasons. From May 2021 to February 2022, a series of longitudinal experiments were conducted in Xiamen, China to examine the effect of wearing surgical masks on outdoor thermal comfort. Forty-two participants took part in the experiments with and without masks. During the experiments, the thermal perceptions of the subjects and environmental thermal parameters were collected. Differences in outdoor thermal comfort between subjects wearing masks and those not wearing masks were determined in summer, autumn, and winter. Results showed that 1) the subjects wearing masks had lower neutral temperatures, and this difference was particularly pronounced in summer and exacerbated by walking; 2) in warm environments, masks reduced thermal comfort, and discomfort associated with masks was worse when walking than when sitting; 3) wearing masks significantly worsened facial comfort and increased chest discomfort, as summer turned to winter, the impact of masks on facial comfort decreased; 4) radiation and air temperature were the environmental parameters with the greatest impact on outdoor thermal sensation. Subjects who wore masks preferred lower temperatures, radiation, and humidity, and higher wind speeds.

How Environmental Factors Affecting COVID-19 Transmission? Case Study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r= -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19's spread in KSA.

A Time-Series Analysis on the Covid-19 Mortality, PM2.5 Levels, and Weather Variables in Denpasar City, Indonesia

The objective of this study is to explore the relationship between Covid-19 mortality cases and environmental variables, namely PM2.5 concentration and weather variables, in Denpasar City, Indonesia. Regression models were used. The response variable was the monthly Covid-19 mortality from March 2020 to December 2021 and the predictor variables were the mean concentration of PM2.5, temperature, wind speed, rainfall and duration of sunshine. All data analyzed were provided by the Indonesian Government. Simple linear regression (SLR) and dynamic regression with ARIMA error models were used. Further, of the 22 monthly data, the first 19 months data were used to train the models and the remaining data were used as the test data. It is found that both wind speed and the interaction between PM2.5 concentration and wind speed have statistically significant relationships with Covid-19 mortality. The estimates of SLR and ARIMA (0,1,1) with interaction models show that on average, in case of 0.5 m/s wind speed, an increase of 1 𝜇g/m3 in the monthly mean of daily PM2.5 concentrations associates with 17.4 and 16.3 increase in the monthly Covid-19 mortality case, respectively. Although this study is observational, its findings suggest the importance of controlling PM2.5 concentration.

Survey and surveillance on rice false smut disease severity in Tamil Nadu and the influence of wind velocity on disease progression under field conditions

The false smut disease of rice incited by Ustilaginoidea virens is a major constraint on rice production. The main aim of the present study was to ascertain the false smut disease severity in both delta and non-delta districts of Tamil Nadu through survey and surveillance and assess the correlation between the disease severity and wind velocity for the past three years, 2019, 2020 and 2021. Moreover, the present study addressed the disease distribution pattern of false smut diseases under field conditions. The results obtained from survey results revealed that the maximum disease severity was recorded in Nagapattinam district (Nagapattinam block) with 27.45% and the minimum disease severity was recorded in the district Theni (Bodinayakanur block) with 8% in 2021. Similarly in 2019 and 2020 maximum disease severity was recorded in the following districts Thanjavur district (Orathanadu block) with 19.91% and Thanjavur district (Peravurani block) with 18.54% and the minimum disease severity was recorded in the following districts Madurai district (Madurai north block) with 4.78% and Madurai district (Usilampatti block) with 4.78% respectively. The obtained R2 values through regression analysis were 0.70, 0.79 and 0.76 in the following years, 2019, 2020 and 2021, respectively. Besides the relationship between the false smut disease development, the pattern wind direction was also assessed. By assessing the false smut disease distribution pattern under field conditions, more disease distribution was observed around the surrounding area of the paddy field as well as the diagonal path of the field which clearly revealed that wind direction influences the disease development. [ FROM AUTHOR] Copyright of Journal of Applied & Natural Science is the property of Applied & Natural Science Foundation 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.)

Short-Term Load Forecasting Using a Parallel CNN-BPNN Prediction Model with COVID-19 Pandemic Restriction as an Added Input Parameter and ReLU Activation Function

Short-term load forecasting provides a vital tool for the power system. This study delved into applying a hybridized machine learning algorithm to improve load forecasting accuracy. It aims to investigate the accuracy of the parallel CNN-BPNN prediction model in short-term load forecasting with Philippine pandemic restriction as an added parameter and a ReLU activation function. The CNN, BPNN, and the proposed parallel CNN-BPNN models were implemented using Python. They were trained, validated, and tested using the input parameters such as historical power demand, day of weeks/ Holidays, meteorological data such as temperature, wind speed, humidity, and COVID-19 pandemic restriction. The accuracy of the three models was tested using the MAPE. Results showed that the proposed model achieved the lowest MAPE of 3.52 %, lower than that of the CNN, 4.62%, and BPNN, 3.98%. Furthermore, Pearson correlation analysis showed that the relationship between electricity usage and mobility constraints is moderately correlated with a correlation value of -0.57. © 2022 WCSE. All Rights Reserved.