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The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

Purpose of review There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future. Recent findings Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new zoonotic agent that emerged in December 2019, causes coronavirus disease 2019 (COVID-19). This infection can be spread by asymptomatic, presymptomatic, and symptomatic carriers. SARS-CoV-2 spreads primarily via respiratory droplets during close person-to-person contact in a closed space, especially a building. This article summarizes the environmental factors involved in SARS-CoV-2 transmission, including a strategy to prevent SARS-CoV-2 transmission in a building environment. SARS-CoV-2 can persist on surfaces of fomites for at least 3 days depending on the conditions. If SARS-CoV-2 is aerosolized intentionally, it is stable for at least several hours. SARS-CoV-2 is inactivated rapidly on surfaces with sunlight. Close-contact aerosol transmission through smaller aerosolized particles is likely to be combined with respiratory droplets and contact transmission in a confined, crowded, and poorly ventilated indoor environment, as suggested by some cluster cases. Although evidence of the effect of aerosol transmission is limited and uncertainty remains, adequate preventive measures to control indoor environmental quality are required, based on a precautionary approach, because COVID-19 has caused serious global damages to public health, community, and the social economy. The expert panel for COVID-19 in Japan has focused on the “3 Cs,” namely, “closed spaces with poor ventilation,” “crowded spaces with many people,” and “close contact.” In addition, the Ministry of Health, Labour and Welfare of Japan has been recommending adequate ventilation in all closed spaces in accordance with the existing standards of the Law for Maintenance of Sanitation in Buildings as one of the initial political actions to prevent the spread of COVID-19. However, specific standards for indoor environmental quality control have not been recommended and many scientific uncertainties remain regarding the infection dynamics and mode of SARS-CoV-2 transmission in closed indoor spaces. Further research and evaluation are required regarding the effect and role of indoor environmental quality control, especially ventilation.

Agastache rugosa (Fisch. & C. A. Mey.) Kuntze has been widely studied because of its high medicinal and edible value. Establishing the priority protected area of wild A. rugosa can provide scientific basis for the protection of germplasm resources. In this study, we predicted the potential suitability distribution area of A. rugosa under the current and future climate scenarios with the MaxEnt model, and the dominant climate factors affecting the distribution of A. rugosa were analyzed. Based on the above results, we predicted the priority protected areas of A. rugosa with the Marxan model. The results showed that A. rugosa is mainly distributed in the eastern and central regions of China at present. In future, the suitable area of A. rugosa will increase, otherwise a few areas will shrink back and migrate to the high latitude areas as a whole. Hydrothermal conditions are the main environmental factors affecting the distribution of A. rugosa . The priority protected areas of A. rugosa are mainly distributed in Chongqing, eastern Sichuan, southern Guizhou, western Hunan and Hubei and southwestern Shaanxi, which are basically consistent with the highly suitable areas predicted by Maxent model. The results of this study are of great significance for the protection and rational utilization of species of Agastache.

Microorganisms play crucial roles in nutrient cycling, water quality maintenance, and farmed animal health. Increasing evidences have revealed a close association between unstable microbial environments and disease occurrences in aquaculture. Thereupon, we used high-throughput sequencing technology to comprehensively compare the bacterial communities of water, sediment, and intestine in mariculture ponds at the middle and late stages of Litopenaeus vannamei farming and analyzed whether changes of their microbiota assemblages were associated with environmental factors and shrimp physiological health. Results showed that bacterial community structures were significantly distinct among water, sediment, and intestine; meanwhile, the relative abundances of intestinal dominant taxa were significantly changed between different rearing stages. Compared with intestine and water, shrimp intestine and sediment had a similar profile of the dominant bacterial genera by cluster analysis, and the observed species, diversity indexes, and shared OTUs of bacterial communities in intestine and sediment were simultaneously increased after shrimp were farmed for 90 days. These results reflected a closer relationship between microbiotas in sediment and intestine, which was further proved by nonmetric multidimensional scaling analysis. However, bacterial communities in water, sediment, and intestine responded differently to environmental variables by redundancy and correlation analysis. More importantly, shrimp physiological parameters were closely associated with bacterial variations in the gut and/or ambient, especially the gut microbiota owning significantly high levels of predicted functional pathways involved in disease emergence. These findings may greatly add to our understanding of the microbiota characteristics of the shrimp pond ecosystem and the complex interactions among shrimp, ambient microflora, and environmental variables.

Sapindus mukorossi ( S . mukorossi ) is an important biological washing material and biomass energy tree species whose peel is rich in saponins, and its kernels have a high oil content. We used the maximum entropy model (MaxEnt) to predict the suitable habitats of S. mukorossi globally, screen the dominant environmental factors affecting its distribution and analyse the changes in its suitable habitats under climate change from prehistory to the future, and the results will provide a scientific basis for germplasm resource collection, protection, introduction and cultivation. Twenty-two environmental variables and global distribution data for S. mukorossi were used to construct the species distribution model, and the receiver operating characteristic (ROC) curve was used to verify the accuracy of the model. The dominant environmental factors were screened through the jackknife method, and then, the geographical information system (ArcGIS) was used to complete the grade of suitable habitat division and area calculation. The results showed that the MaxEnt model had an excellent predictive effect, and the area under the ROC curve (AUC) value was as high as 0.969. The precipitation of the warmest quarter (Bio18), the minimum temperature of the coldest month (Bio6), temperature seasonality (Bio4) and isothermality (Bio3) were the dominant environmental factors that affected the distribution of S. mukorossi . The largest area of the world’s suitable habitats occurred during the last interglacial (LIG) (772.69 × 10 4 km 2 ), and the area decreased sharply (614.46 × 10 4 km 2 ) during the last glacial maximum (LGM). The area of suitable habitat showed an increasing trend during the Mid-Holocene (MH) and currently, with areas of 631.06 × 10 4 km 2 and 706.82 × 10 4 km 2 , respectively. The area of suitable habitats for S. mukorossi globally was 718.35 × 10 4 km 2 (SSP1-2.6), 636.85 × 10 4 km 2 (SSP2-4.5), 657.64 × 10 4 km 2 (SSP3-7.0) and 675.89 × 10 4 km 2 (SSP5-8.5) under the four scenarios of the future climate. The area increased only in the SSP1 scenario. In summary, globally, the suitable area of S. mukorossi tended to migrate to higher latitudes and decrease in area with future climate change.

The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006–2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation, and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations are noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multi-polarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE, and SE; these included energy structure optimization, labor force number, total power of construct ion equipment, and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.

We investigated the heritability of educational attainment and how it differed between birth cohorts and cultural–geographic regions. A classical twin design was applied to pooled data from 28 cohorts representing 16 countries and including 193,518 twins with information on educational attainment at 25 years of age or older. Genetic factors explained the major part of individual differences in educational attainment (heritability: a 2  = 0.43; 0.41–0.44), but also environmental variation shared by co-twins was substantial (c 2  = 0.31; 0.30–0.33). The proportions of educational variation explained by genetic and shared environmental factors did not differ between Europe, North America and Australia, and East Asia. When restricted to twins 30 years or older to confirm finalized education, the heritability was higher in the older cohorts born in 1900–1949 (a 2  = 0.44; 0.41–0.46) than in the later cohorts born in 1950–1989 (a 2  = 0.38; 0.36–0.40), with a corresponding lower influence of common environmental factors (c 2  = 0.31; 0.29–0.33 and c 2  = 0.34; 0.32–0.36, respectively). In conclusion, both genetic and environmental factors shared by co-twins have an important influence on individual differences in educational attainment. The effect of genetic factors on educational attainment has decreased from the cohorts born before to those born after the 1950s.

Activated sludge system is an important process of domestic and industrial wastewater treatment containing highly diverse microbial communities. In this study, high-throughput sequencing was applied to examine the microbial community composition and diversity of activated sludge samples from four full-scale municipal wastewater treatment plants (WWTPs) in Shanghai. A relationship between microbial communities and environmental variables was examined. Proteobacteria was the most dominant phylogenetic group, followed by Bacteroidetes and Firmicutes . A total of 166 genera were commonly shared by all seven sludge samples, including Zoogloea , Dechloromonas , Thauera , Nitrospira , Arcobacter , etc. Besides these shared populations, certain unique bacterial populations were found abundant in individual sludge sample. Canonical correspondence analysis (CCA) indicated that influent COD and pH had the greatest influence on microbial community compositions, whereas dissolved oxygen (DO) exhibited the least influence. The operating process was likely to foster diversity of the microbial communities inhabiting the wastewater treatment facilities. Alternative operation methods including a fluctuation of anoxic, oxic, and anaerobic conditions were favorable for promoting the growth of diverse microbial populations in activated sludge systems.

Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of − 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread. Graphical abstract