Environmental perspective of COVID-19: Atmospheric and wastewater environment in relation to pandemic

A systematic review on environmental perspectives of monkeypox virus

Monkeypox (MPX) is one of the common infections between humans and animals that caused by a virus belonging to the Orthopoxvirus genus. The Monkeypox virus (MPXV) outbreak is a global crisis triggered by environmental factors (virus, wastewater, surface, air) and amplified by the decisions of government officials and communities. The aim of this systematic review is to describe the environmental perspectives of MPXV with emphasis on risk assessment to prevent and control a new pandemic. Five online databases including Web of Science, PubMed, Scopus, Science Direct and Google Scholar were searched from 1990 to October 2022. Among 120 records, after the screening, four studies were included in the systematic review. The systematic review revealed that the possibility of MPXV transmission through wastewater, air, and the contaminated surfaces is a significant concern and its detection and destroying will play a major role in controlling the spread of the virus. Poxviruses have a high environmental stability, but are sensitive to all common chemical disinfectants. In conclusion, this study revealed that the environmental surveillance can be used as a complementary tool for detecting pathogens circulation in communities. This implies that the monitoring of environmental perspectives of MPXV can provide new awareness into virus transmission routes as well as the role of stakeholders and public health policies in MPXV risk management.

Is SARS-CoV-2 a concern in the largest wastewater treatment plant in middle east?

The surveillance of wastewater treatment plant (WWTP) as the end point of SARS-CoV-2 shed from infected people arise a speculation on transmission of this virus of concern from WWTP in epidemic period. To this end, the present study was developed to comprehensively investigate the presence of SARS-CoV-2 in raw wastewater, effluent and air inhaled by workers and employee in the largest WWTP in Tehran for one-year study period. The monthly raw wastewater, effluent and air samples of WWTP were taken and the SARS-CoV-2 RNA were detected using QIAamp Viral RNA Mini Kit and real-time RT-PCR assay. According to results, the speculation on the presence of SARS-CoV-2 was proved in WWTP by detection this virus in raw wastewater. However, no SARS-CoV-2 was found in both effluent and air of WWTP; this presents the low or no infection for workers and employee in WWTP. Furthermore, further research are needed for detection the SARS-CoV-2 in solid and biomass produced from WWTP processes due to flaks formation, followed by sedimentation in order to better understand the wastewater-based epidemiology and preventive measurement for other epidemics probably encountered in the future.

The outbreak of COVID-19 in Taiwan in late spring 2021: combinations of specific weather conditions and related factors

This study aimed to investigate the impact of weather conditions on the daily incidence of the COVID-19 pandemic in late spring 2021 in Taiwan, which is unlike the weather conditions of the COVID-19 outbreak in 2020. Meteorological parameters such as maximum daily temperature, relative humidity, and wind speed were included. The Spearman rank correlation test was used to evaluate the relationship between weather and daily domestic COVID-19 cases. The maximum daily temperature had a positively significant correlation with daily new COVID-19 cases within a 14-day lag period, while the relative humidity and wind speed has a fairly high correlation with the number of daily cases within a 13- and 14-day lag, respectively. In addition, the weather characteristics during this period were an increasingly high temperature, with steady high relative humidity and slightly decreasing wind speed. Our study revealed the weather conditions at the time of the domestic outbreak of COVID-19 in Taiwan in May 2021 and the possible association between weather factors and the COVID-19 pandemic. Further large-scale analysis of weather factors is essential for understanding the impact of weather on the spread of infectious diseases.

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular, estimating the patient numbers and achieving an informed management of the dilemma are crucial in terms of the strategic decisions to be taken. The Covid-19 pandemic presents an important case in this context. Sustaining the efforts to cope with and to put an end to this pandemic requires investigation of the spread and infection mechanisms of the disease, and the factors which facilitate its spread. Covid-19 symptoms culminating in respiratory failure are known to cause death. Since air quality is one of the most significant factors in the progression of lung and respiratory diseases, it is aimed to estimate the number of Covid-19 patients corresponding to the pollutant parameters (PM10, PM2.5, SO₂, NO_X, NO₂, CO, O₃) after determining the relationship between air pollutant parameters and Covid-19 patient numbers in Turkey. For this purpose, artificial neural network was used to estimate the number of Covid-19 patients corresponding to air pollutant parameters in Turkey. To obtain highest accuracy levels in terms of network architecture structure, various network structures were tested. The optimal performance level was developed with 15 neurons combined with one hidden layer, which achieved a network performance level as high as 0.97342. It was concluded that Covid-19 disease is affected from air pollutant parameters and the number of patients can be estimated depending on these parameters by this study. Since it is known that the struggle against the pandemic should be handled in all aspects, the result of the study will contribute to the establishment of environmental decisions and precautions.

Environmental factors and mobility predict COVID-19 seasonality in the Netherlands

BACKGROUND

We recently showed that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands. We hypothesize that in The Netherlands the same environmental factors and mobility trends that are associated with the seasonality of flu-like illnesses are predictors of COVID-19 seasonality as well.

METHODS

We used meteorological, pollen/hay fever and mobility data from the Netherlands. For the reproduction number of COVID-19 (R_t), we used daily estimates from the Dutch State Institute for Public Health. For all datasets, we selected the overlapping period of COVID-19 and the first allergy season: from February 17, 2020 till September 21, 2020 (n = 218). Backward stepwise multiple linear regression was used to develop an environmental prediction model of the R_t of COVID-19. Next, we studied whether adding mobility trends to an environmental model improved the predictive power.

RESULTS

Through stepwise backward multiple linear regression four highly significant (p < 0.01) predictive factors are selected in our combined model: temperature, solar radiation, hay fever incidence, and mobility to indoor recreation locations. Our combined model explains 87.5% of the variance of R_t of COVID-19 and has a good and highly significant fit: F(4, 213) = 374.2, p < 0.00001. This model had a better overall predictive performance than a solely environmental model, which explains 77.3% of the variance of R_t (F(4, 213) = 181.3, p < 0.00001).

CONCLUSIONS

We conclude that the combined mobility and environmental model can adequately predict the seasonality of COVID-19 in a country with a temperate climate like the Netherlands. In this model higher solar radiation, higher temperature and hay fever are related to lower COVID-19 reproduction, and higher mobility to indoor recreation locations is related to an increased COVID-19 spread.

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy

Background

Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM₁₀, PM_2.5, NO₂, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic.

In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information.

Methods

We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)². We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model.

Findings

Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM_2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags.

Interpretation

ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)², getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Emerging Pollutants in Wastewater, Advanced Oxidation Processes as an Alternative Treatment and Perspectives

Emerging pollutants are present in wastewaters treated by conventional processes. Due to water cycle interactions, these contaminants have been reported in groundwater, surface water, and drinking waters. Since conventional processes cannot guarantee their removal or biotransformation, it is necessary to study processes that comply with complete elimination. The current literature review was conducted to describe and provide an overview of the available information about the most significant groups of emerging pollutants that could potentially be found in the wastewater and the environment. In addition, it describes the main entry and distribution pathways of emerging contaminants into the environment through the water and wastewater cycle, as well as some of the potential effects they may cause to flora, fauna, and humans. Relevant information on the SARS-CoV-2 virus and its potential spread through wastewater is included. Furthermore, it also outlines some of the Advanced Oxidation Processes (AOPs) used for the total or partial emerging pollutants removal, emphasizing the reaction mechanisms and process parameters that need to be considered. As well, some biological processes that, although slow, are effective for the biotransformation of some emerging contaminants and can be used in combination with advanced oxidation processes.

Medical Waste from COVID-19 Pandemic-A Systematic Review of Management and Environmental Impacts in Australia

The coronavirus (COVID-19) pandemic has created a global medical emergency. The unforeseen occurrence of a pandemic of this magnitude has resulted in overwhelming levels of medical waste and raises questions about management and disposal practices, and environmental impacts. The amount of medical waste generated from COVID-19 since the outbreak is estimated to be 2.6 million tons/day worldwide. In Australia, heaps of single-use gowns, facemasks/face shields, aprons, gloves, goggles, sanitizers, sharps, and syringes are disposed everyday as a result of the pandemic. Moreover, the establishment of new home/hotel quarantine facilities and isolation/quarantine centres in various Australian states and territories have increased the risks of transmission among people in these facilities and the likelihoods of general waste becoming contaminated with medical waste. This warrants the need to examine management and disposal practices implemented to reduce the transmission and spread of the virus. This study reviews the various management and disposal practices adopted in Australia for dealing with medical waste from the COVID-19 pandemic and their impacts on public health and the environment. To achieve the aims of this study, prior studies from 2019-2021 from various databases are collected and analysed. The study focuses on generation of medical waste from COVID-19, management and disposal methods, current problems/challenges and environmental and public health impacts. Considering the enormous risks involved and the significance of appropriate handling and disposal of medical waste from COVID-19, this study provides insights on short and long term responses towards managing COVID-19 waste in Australia. The study contributes to Australia's efforts against the transmission and spread of COVID-19 and provides recommendations for the development of workable and sustainable strategies for mitigating similar pandemics in the future.

Walkable City and Military Enclaves: Analysis and Decision-Making Approach to Support the Proximity Connection in Urban Regeneration

Accessibility and urban walkability are the cornerstones of urban policies for the contemporary city, which needs to be oriented towards sustainable development principles and models. Such aims are included in the objectives of the 2030 Agenda, as well as in the ambitious objectives of the ‘European Green Deal’. These concepts are closely linked to the paradigm of a sustainable city—livable, healthy and inclusive—based on a system of high-quality public spaces and on a network of services and infrastructures, both tangible and intangible, capable of strengthening and building new social, economic and environmental relationships. It is necessary to recognize potential opportunities for connection and permeability in consolidated urban environments. These are very often fragmented and are characterized by enclaves of very different kinds. Ghettoes and gated communities, old industrial plants and military installations and facilities, to cite a few, represent examples of cases where closures on urban fabrics are realized, impeding full walkability and accessibility. Within such a framework, the present research is aimed at focusing on a particular set of enclaves, such as those represented by the military sites being reconfigured to civilian use, a phenomenon that characterizes many urban areas in the world; in Europe; and in Italy, in particular, given the recent history and the Cold War infrastructure heritage. In such a sense, the city of Cagliari (Sardinia Island, Italy) represents an interesting case study as it is characterized by the presence of a series of military complexes; real ‘enclaves’ influencing the proximity connections; and, more generally, walkability. Building on previous research and analysis of policies and projects aimed at reintroducing, even partially, this military asset into civilian life (Green Barracks Project (GBP)-2019), this paper proposes and applies a methodology to evaluate the effects of urban regeneration on walkability in a flexible network logic, oriented to the ‘15 min city’ model or, more generally, to the renewed, inclusive, safe “city of proximity”, resilient and sustainable.