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PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
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From satellites to ground-based sensors, as well as mobile networks of monitors, the availability of massive data sets has increased the need for educating students in data literacy in order to ensure their competency in the global market (Bluhm et al. 2020;Gibson and Mourad 2018). The U.S. Environmental Protection Agency (EPA) defines environmental justice as, "... the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies." According to Lacombe, more individuals die yearly from car exhaust (53,000) in the United States than road casualties (37,400). Students worked in groups to discuss their assumptions about factors they perceived to have an impact on air pollution levels (e.g., affluence, traffic, and vegetation).
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The COVID-19 pandemic has highlighted the importance of incorporating nature-based solutions in urban design, in order to create sustainable and resilient cities. Inspired by these events, the present study aims at exploring the mental health benefits of nature exposure during the outbreak. Secondarily, we investigate changes in use patterns towards urban green spaces (UGS) and urban blue spaces (UBS) and whether extreme conditions, such as these of a lockdown, can lead to an increase in people's appreciation of urban nature. Through an online survey, we observed that the pandemic resulted in a decrease in the frequency of visitation to UGS/UBS (p < 0.001). Significant differences were found for exercise (p < 0.001) and socialization (p < 0.05) as main drivers for visiting urban nature pre- and post-lockdown. Accordingly, visitation rates for forests (p < 0.05), playgrounds (p < 0.001), and the sea (p < 0.001) differed significantly when comparing the two periods. In people's perception, UGS/UBS are important for the urban fabric (89%). Our structural equation model indicated that nature exposure had a beneficial effect on participants' mental health (p < 0.001). Pathways that explain the relationship between nature exposure and post- lockdown value were nature relatedness, motivation, and perceived importance of UGS/UBS. No mediation could be extracted for nature exposure and mental health. Our findings show the positive association between nature exposure and mental health improvement, especially in times of crisis, as well as a shift in the "value domain” towards urban nature.
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Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.
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PurposeThis study aims to review the role of green training and green work life balance (GWLB) on sustainable organizational performance (SOP) with a moderating variable "Emotional Intelligence” (EI).Design/methodology/approachFor the development of the construct of the present study, a Scopus database was selected and research papers published in indexed journals were considered. Relevant keywords were selected and literature was searched on green training, EI, SOP, GWLB. The literature was reviewed to find out the linkage and possibility of development of integrated model. The main focus was on highlighting the relevance of green training on GWLB and its influence on SOP.FindingsSOP can be achieved with the intervention of EI and GWLB;further green training is one of the influential practices of human resource development (HRD) which helps to develop the green behavior.Research limitations/implicationsIt can give new insight to the organization for application of green human resource practices for SOP. Development and designing the cohesive environmental work culture and willingness to protect environment through green training can be implemented by HRD. Perhaps, the application of green training encourages GWLB.Practical implicationsQuantitative research and cross sectional study is required to find out the intervening role of EI and work–life balance between green training and SOP across a broader range of sectors.Originality/valueThis research extends the literature review and developed a new integrated model which shows the link between green training and SOP.
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Air quality and environmental justice To introduce how socioeconomic status affects the physical aspects of exposure to differing air-quality levels, students used an anthropological technique of comparison to "make the strange familiar and the familiar strange." Students analyzed a New York Times story revealing the air-quality inequities of two teens residing in India: "Who Gets to Breathe Clean Air in New Delhi?" For 25 minutes, students interact with the website and reflect on paper: * One new and interesting fact that they encountered in the article about air quality, * How they think the information might relate to air quality in the United States, and * What, if anything, they think we could do to help increase awareness about these types of environmental disparities. For the next 35 minutes, students search online for articles about air quality and environmental justice in the area near our school's location. The data from real-time air quality index reports are available on every cell phone, and students decided to record it on a calendar to chart in Excel.
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Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.
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Public libraries are popular gathering places, so understanding the factors that contribute to colony-forming unit (CFU) concentrations and how to minimize them is essential. This study aimed to investigate the factors that affect CFU concentrations in a public library, using air sampling (Bioluminescent ATP-assay) and statistical analysis software (SPSS) to collect and analyze data. The findings indicated that the CFU concentration in the library was significantly influenced by the air quality surrounding the building, the number of library visitors, and the hygiene and health of both visitors and employees. Additionally, indoor temperature and humidity were found to be key factors affecting CFU concentration. These findings suggest the need for better ventilation and air filtration systems, as well as regular cleaning and disinfection in public libraries. Furthermore, research is recommended to investigate other potential factors that may impact indoor air quality in public spaces.
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Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m-3 in the North China Plain (NCP) and 23 µg m-3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.
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To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.
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The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.
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Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.
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Coronavirus disease 2019 (COVID-19) swept the world at the beginning of 2020, and strict activity control measures were adopted in China's concentrated and local outbreak areas, which led to social shutdown. This study was conducted in southwest China from 2019 to 2021, and was divided into the year before COVID-19 (2019), the year of COVID-19 outbreak (2020), and the year of normalization of COVID-19 prevention and control (2021). A geographically and temporally weighted regression (GTWR) model was used to invert the spatial distribution of PM2.5 by combining PM2.5 on-site monitoring data and related driving factors. At the same time, a multiple linear regression (MLR) model was constructed for comparison with the GTWR model. The results showed that: (1) The inversion accuracy of the GTWR model was higher than that of the MLR model. In comparison with the commonly used PM2.5 datasets "CHAP” and "ACAG”, PM2.5 inverted by the GTWR model had higher data accuracy in southwest China. (2) The average PM2.5 concentrations in the entire southwest region were 32.1, 26.5, and 28.6 μg/m3 over the three years, indicating that the society stopped production and work and the atmospheric PM2.5 concentration reduced when the pandemic control was highest in 2020. (3) The winter and spring of 2020 were the relatively strict periods for pandemic control when the PM2.5 concentration showed the most significant drop. In the same period of 2021, the degree of control was weakened, and the PM2.5 concentration showed an upward trend.
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Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.
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The purpose of this study was to measure the number and concentration of airborne particulates occurring in a dental clinic while performing dental procedures, with and without the simultaneous use of air purifier systems and a central ventilation system. The initial background concentrations of airborne particulates recorded during dental procedures, i.e., grinding of natural teeth and metals, without the use of air purifier systems, and with closed windows, reduced by 68% for ΡΜ10, 77% for ΡΜ2.5, and 81% for ΡΜ1 when the same procedures were carried out with the simultaneous use of air purifying systems. In addition, measurements taken during patient treatment showed that an operating central ventilation system contributes to the reduction of airborne particles by a significant 94% for ΡΜ10, 94% for ΡΜ2.5, and 88% for ΡΜ1 compared to dental procedures performed without the simultaneous use of air purifiers. Air purifying systems were also observed to contribute to the further reduction of airborne particulates when dental procedures were performed in combination with an operating central ventilation system. The majority of particles captured had diameters of 0.25-0.30 μm, 0.5 μm, and 1.0-4.0 μm, while particles with diameters of >5.0 μm were the least commonly observed in all experiments. Finally, a statistically significant difference between concentrations of particulate matter was recorded during dental procedures carried out with and without the simultaneous operation of air purifiers and central ventilation system increasing the risk of SARS-CoV-2 virus contamination in dental clinics due to the aerosols emitted by the use of common dental instruments during standard treatments.
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Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a novel UVC-LED (light-emitting diode) air purifier for a low-cost solution to mitigate airborne diseases is proposed. The concept focuses on performance and robustness. It contains a dust-filter assembly, an innovative UVC chamber, and a fan. The low-cost dust filter aims to suppress dust accumulation in the UVC chamber to ensure durability and is conceptually shown to be easily replaced while mitigating any possible contamination. The chamber includes novel turbulence-generating grids and a novel LED arrangement. The turbulent generator promotes air mixing, while the LEDs inactivate the pathogens at a high flow rate and sufficient kill rate. The conceptual design is portable and can fit into ventilation ducts. Computational fluid dynamics and UVC ray methods were used for analysis. The design produces a kill rate above 97% for COVID and tuberculosis and above 92% for influenza A at a flow rate of 100 L/s and power consumption of less than 300 W. An analysis of the dust-filter performance yields the irradiation and flow fields.
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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.
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This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.
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If it is not adequately managed, the waste from healthcare facilities containing infectious material poses a risk to the general public and the natural environment. As a result, hospitals must ensure that their waste management policies do not add to the dangers posed to both human health and the environment. In this study, we aimed to determine the effect that varying doses of disinfectant in conjunction with andosol soil had on the total number of bacteria present in the medical waste generated by three hospitals in Semarang City, Indonesia. According to the findings of the study, the most efficient method for decreasing the overall number of microbial colonies by 93% was a combination involving soil (at a percentage of 30) and chlorine (at a concentration of 0.75 ppm). As a consequence of this, and due to the limited technology available, this straightforward method can become an alternative for the healthcare industry in managing medical waste before dumping or incinerating it. Hospitals have been advised to discontinue the practice of directly burning, disinfecting, or transporting waste to disposal locations before it receives treatment. This can help reduce the risk of pandemics, as the correct disposal of medical waste can control infection sources.
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With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.