Global Journal of Environmental Science and Management
Volume:6 Issue: 3, Autumn 2020

Currently, the pandemic caused by a novel coronavirus, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the most serious issues worldwide. SARS-CoV-2 was first observed in Wuhan, China, on December 31, 2019; this disease has been rapidly spreading worldwide. Iran was the first Middle East country to report a coronavirus death, it has been severely affected. Therefore, it is crucial to forecast the pandemic spread in Iran. This study aims to develop a prediction model for the daily total confirmed cases, total confirmed new cases, total deaths, total new deaths, growth rate in confirmed cases, and growth rate in deaths. The model utilizes SARS-CoV-2 daily data, which are mainly collected from the official website of the European Centre for Disease Prevention and Control from February 20 to May 04, 2020 and other appropriated references. Autoregressive integrated moving average (ARIMA) is employed to forecast the trend of the pandemic spread. The ARIMA model predicts that Iran can easily exhibit an increase in the daily total confirmed cases and the total deaths, while the daily total confirmed new cases, total new deaths, and growth rate in confirmed cases/deaths becomes stable in the near future. This study predicts that Iran can control the SARS-CoV-2 disease in the near future. The ARIMA model can rapidly aid in forecasting patients and rendering a better preparedness plan in Iran.

Origin of the coronavirus was the seafood market of Wuhan city, Hubei province in China. The cases of someone suffering from COVID-19 can be traced back to the end of December 2019 in China. This is the most infectious disease and spread worldwide within three months after the first case reported. The World Health Organization renames Coronavirus as COVID-19. COVID-19 is the β-Coronavirus family virus, effect on the lung of human and common symptoms are cough, fever, fatigue, respiratory problem, and cold. The full name of the coronavirus is severe acute respiratory syndrome SARS-CoV. It spread on humans as well as animals and infected more than 183 countries with 2959927 confirm cases and 202733 deaths till 28 April 2020. 84 days data is used to predict confirmed and death cases for the next 10 days by using prophet and daily average based algorithm. Predicted confirmed cases are 2886183 and death cases 190540 till 25 April 2020. This study introduces the spreading pattern of COVID-19 in the top ten infected countries.  After China, European countries are the most infected ones. In this study, data was analyzed on the attributes confirmed, active, recovered and death cases, and next ten days outbreak prediction. Some countries state-wise data confirmed active and death cases also analyzed.

One of thesources of infection as a result of coronavirus disease treatment is the medical waste generated during the health care activities. Since the registration of the first infected case of coronavirus in Jordan the daily number of patients fluctuated from as low as zero to as high as 40 with a recovery ratio and case fatality risk of 39% and 1.7%, respectively. The main objective of the present study is to carry out statistical analysis and assess the generation rates and the composition of the medical waste generated during the treatment of coronavirus pandemic with reference to a major tertiary care hospital in Jordan. Data onthe daily generated waste, number of the admitted patients and on the amounts of consumables like various personal protective equipment, testing kits, and disinfectant used during the treatment of coronavirus disease was obtained. Data was subjected to descriptive statistical analysis to find the average generation rates, 3 days moving average, as well as the frequency distribution of the generated amounts. During 25 days' period, King Abdullah University Hospital has admitted 95 infected patients by coronavirus. The amount of the average rate of the medical waste generated as a result of coronavirus treatment was found to be 14.16 kg/patient/day and 3.95 kg/bed/day, which are more than tenfold higher than the average generation rate during the regular operational days of the hospital. Frequency analysis of the data revealed that the medical waste generation follows log normal distribution with correlation coefficient of 0.89.  The distribution is distorted to the right and flatter than the normal distribution curve as judged by the skewness and kurtosis coefficients, respectively, which indicates deviation from normality.

COVID-19 is a huge tragedy for the world community. Everything in the world is affected due to this pandemic right from economy to resources where the economy of major countries of the world are facing recession and resources are surplus with no takers at all. The measures to contain COVID-19 pandemic include lockdown, social distancing, isolation, and home quarantine. Lockdown adopted by the different governments which involve non-functioning of all the industry and manufacturing units. However, as a blessing in disguise, these measures have a positive effect on the environment in terms of reduction in toxic gasses like nitrogen dioxide, aerosols, atmosphere ozone, particulate matter, and improvement in air quality. In this paper, the effect on various environmental parameters like aerosol, ozone, particulate matter, nitrogen dioxide, sulfur dioxide, carbon monoxide, and temperature on India by lockdown due to COVID-19 as a preventive measure has been analyzed.  The work involves the refining and preprocessing of raw data of this year and last year of various harmful pollutants present in the environment along with satellite images from National Aeronautics and Space Administration for comparison of different parameters. It has been observed that with the above adopted measures temperature has been reduced to near about 15 degree Celsius, there is also reduction in humidity i.e. it is reduced to 40%,  particulate matter (PM2.5)  reaches near about normal i.e. 40 g/m3 and carbon monoxide levels has also been  reduced to 10 ppm. The main idea is to emphasize the fact that how the environment is self-healing during the lockdown. And this study will be beneficial to environmentalists and industry professionals to make the future strategy for improving the environment.

Houshold waste is the residue generated daily by people as a result of consuming goods and services. The qualitative and quantitative aspects depend on the lifestyle and standard of living of citizens. Hence a change in habits, following an economic or health crisis, can influence the production of waste and its composition. The objective of the present work is to assess the impact of lockdown on the generation of trash and on the habits related to the consumption of goods in two communes in Morocco. More specifically, this study would investigate the behavior of citizens with regard to protective equipment against the coronavirus COVID-19. The results of the survey show that there is an influence of lockdown on the items purchased during this period, with an increase in the purchase of disinfectant products and a decrease in the consumption of meat and canned goods. Thus, the results showed that the quantity of organic fractions had decreased in the domestic waste with the appearance of other fractions such as residues of cleaning products. In addition, the survey conducted showed that 87% of respondents mix coronavirus protective equipment with household waste, which may contribute to the spread of the virus. Concerning the quantitative aspect, the weigh-ups showed that the monthly rate of increase of waste production between the months of February and March 2019 and the corresponding period in 2020 have decreased from +11.41% to +3.8%  in the city of Khenifra (from 2,572 ton in Mars 2019 to  2,456 ton in the correspondent period in 2020) and from +4.73% to -1.23% in the center of Tighassaline (from 136 ton in Mars 2019 to 123 ton in the correspondent period in 2020).

Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Covid-19 number of rising cases and death cases in India, USA, France, and UK, considering the progressive trends of China and South Korea. In this paper, three cases are considered to analyze the outbreak of Covid-19 pandemic viz., (i) forecasting as per the present trend of rising cases of different countries (ii) forecasting of one week following up with the improvement trends as per China and South Korea, and (iii) forecasting if followed up the progressive trends as per China and South Korea before a week. The results have shown that ANN can efficiently forecast the future cases of COVID 19 outbreak of any country. The study shows that the confirmed cases of India, USA, France and UK could be about 50,000 to 1,60,000, 12,00,000 to 17,00,000, 1,40,000 to 1,50,000 and 2,40,000 to 2,50,000 respectively and may take about 2 to 10 months based on progressive trends of China and South Korea.  Similarly, the death toll for these countries just before controlling could be about 1600 to 4000 for India, 1,35,000 to 1,00,000 for USA, 40,000 to 55,000 for France, 35,000 to 47,000 for UK during the same period of study.

COVID-19 has a severe and widespread impact, especially in Indonesia. COVID-19 was first reported in Indonesia on March 03, 2020 then rapidly spread to all 34 provinces by April 09, 2020. Since then, COVID-19 is declared a state of national disaster and health emergency. This research analyzes the difference of CO, HCHO, NO2, and SO2 density in Jakarta, West Java, Central Java, and South Sulawesi before and during the pandemic. Also, this study assesses the effect of large scale restrictions on the economic growth during COVID-19 pandemic in Indonesia. In a nutshell, the results on Wilcoxon and Fisher test by significance level α=5% as well as odds ratio showed that there are significant differences of CO density in all regions with highest odds ratio in East Java (OR=9.07), significant differences of HCHO density in DKI Jakarta, East Java, and South Sulawesi. There are significant differences of NO2 density before and during public activities limitation in DKI Jakarta, West Java, East Java, and South Sulawesi. However, the results show that there are no significant differences of SO2 density in all regions. In addition, this research shows that there are significant differences of retail, grocery and pharmacy, and residental mobility before and during the COVID-19 pandemic in Indonesia. This research also shows that during the COVID-19 pandemic there are severe economic losses, industry, companies, and real disruptions are severe for all levels of life due to large scale restrictions.

Air pollution has become a serious concern for its potential health hazard, however, often got less attention in developing countries, like Bangladesh. It is expected that worldwide lockdown due to COVID-19 widespread cause reduction in environmental pollution in particularly the air pollution: however, such changes have been different in different places. In Chittagong, a city scale lockdown came in force on 26 March 2020, a week after when first three cases of COVID-19 have been reported in Bangladesh. This study aims to statistically evaluate the effects of COVID-19 lockdown (26 March to 26 April 2020) on selected air quality pollutants and air quality index s. The daily average concentrations of air pollutants PM10, PM2.5, NO2, SO2 and CO of Chittagong city during COVID-19 lockdown were statistically evaluated and were compared with dry season data averaging over previous 8 years (2012 to 2019). During lockdown, except NO2, all other pollutants studied showed statistically significant decreasing trend. During the COVID-19 shutdown notable reduction of 40%, 32% and 13% compared to the daily mean concentrations of these previous dry season were seen for PM2.5, PM10 and NO2, respectively. The improvement in air quality index value was found as 26% in comparison to the previous dry season due to less human activities in COVID-19 shutdown. The factor analysis showed that AQI in Chittagong city is largely influenced by PM10 and PM2.5 during COVID-19 shutdown. The lesson learnt in this forced measure of lockdown is not surprising and unexpected. It is rather thought provoking for the decision makers to tradeoff the tangible air quality benefits with ongoing development strategies’ that was often overlooked directly or indirectly.

At the end of 2019, the new virus called Coronavirus Disease (Covid-19) spread widely from China all over the world. In March 2020 the World Health Organization declared a new virus outbreak as “a global pandemic”, and recommended social distancing and quarantine. Most countries in Europe have been quarantined. The social aspect of this issue is complicated by the fact that Europe nowadays hosts 82 million international migrants. If migrant workers leave the host country, it reduces the Covid-19 spread. Nevertheless, if migrant workers do not return, it will worsen the situation with the economic crisis. The subject of the study is the instrumental and mathematical aspects of impact simulation of labor migrants’ policy on the economic growth of the host country affected by COVID-19 pandemic. The aim of the work is to develop the system dynamics model for assessing labor migrants’ policy impact on the economic growth of the host country during COVID-19 pandemic. It examined through hypotheses of different scenarios of labor migrants policy impact on the host country economic growth in Covid-19 pandemic. The proposed model combines epidemiological and the economic growth models and relies upon real statistical data. The analysis was carried out in four European countries. The results of the study enabled to state that without migrant workers the gross domestic product may fall to 43% in Italy, 45% in Netherlands, 37% in Spain and 200% in Switzerland in 2020.

The coronavirus disease 2019 (COVID-19) has been identified as the main cause of the outbreak of the respiratory disease in Wuhan, Hubei Province of China in December 2019. Since then, the epidemic has spread rapidly throughout China and many other countries in the world. This study, therefore, examines the spatiotemporal distribution of the confirmed cases of COVID-19 and its effect on human development in China, and suggested social and non-pharmaceutical preventive interventions to help curb the further spread of the disease. The public open data available from January to February 2020, from the National Health Commission of the People’s Republic of China and a medical knowledge sharing website were used, and spatial analysis was performed to visualize the spatial distribution pattern of COVID-19 in China. The results showed among others that COVID-19 had entered a dispersed spatial pattern, resulting in increased pressure to control the spread of the disease. In early March, there was a significant reduction in the existing number of cases, and the number of deaths also decreased. At the provincial level, the spatial distribution of the number of cumulative confirmed cases in China was divided into four patterns: Hubei was the initial core region; the eastern provinces adjacent to Hubei formed the second concentrated pattern; the western provinces adjacent to Hubei and the northeastern and southeastern provinces which were separated from Hubei by one province belonged to the third distribution pattern; while the rest of the provinces in the north, south and west showing sporadic distribution patterns formed the fourth. It has been estimated that about 80% of students’ online learning at all schools were not effective due to lack of access to reliable and uninterrupted internet services especially in the rural areas of China.