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"Environmental Factors"
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Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review
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.
Journal Article
Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model
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.
Journal Article
Seeing the forest for the trees : forests, climate change, and our future
\"The tenth of November 1888 was a fine autumn day before the dreary span of winter in Washington, DC. The afternoon was warm, one of several days with pleasant temperatures, and there was only a trace amount of rain.1 The agreeable weather likely lifted the spirits of Washingtonians, and as night fell and the sky darkened and the temperature dropped, there was an air of excitement and anticipation among leading intellectuals in the nation's capital. Nearly three hundred years of European settlement had greatly altered the landscape of the United States.2 The Virginia and New England countryside found by the first English colonists contained extensive old-growth forest. Thereafter, the landscape was cleared of forests, felled for their wood products and replaced by homesteads, towns, and farmland. The vast virgin forests of New England - towering white pines, giants of the forest, famed for the masts they had provided the navy; mixtures of beech, maple, and birch along with hemlock and spruce in northern states; oaks and hickories in southern New England - had long since been felled.3 Similar widespread clearing had altered the forests of Virginia and other states along the eastern seaboard, and the same pattern of forest clearing was repeated in the Midwest and Great Lakes regions with the westward expansion of the population.4 A popular belief at the time was that deforestation was decreasing rainfall\"-- Provided by publisher.
Book
Ecotype‐specific phenolic acid accumulation and root softness in Salvia miltiorrhiza are driven by environmental and genetic factors
Summary Salvia miltiorrhiza Bunge, a renowned medicinal herb in traditional Chinese medicine, displays distinctive root texture and high phenolic acid content, traits influenced by genetic and environmental factors. However, the underlying regulatory networks remain unclear. Here, we performed multi‐omics analyses on ecotypes from four major Chinese regions, focusing on environmental impacts on root structure, phenolic acid accumulation and lignin composition. Lower temperatures and increased UV‐B radiation were associated with elevated rosmarinic acid (RA) and salvianolic acid B (SAB) levels, particularly in the Sichuan ecotype. Structural models indicated that the radial arrangement of xylem conduits contributes to greater root hardness. Genomic assembly and comparative analysis of the Sichuan ecotype revealed a unique phenolic acid metabolism gene cluster, including SmWRKY40, a WRKY transcription factor essential for RA and SAB biosynthesis. Overexpression of SmWRKY40 enhanced phenolic acid levels and lignin content, whereas its knockout reduced root hardness. Integrating high‐throughput (DNA affinity purification sequencing) and point‐to‐point (Yeast One‐Hybrid, Dual‐Luciferase and Electrophoretic Mobility Shift Assay) protein‐DNA interaction detection platform further identified SmWRKY40 binding sites across ecotypes, revealing specific regulatory networks. Our findings provide insights into the molecular basis of root texture and bioactive compound accumulation, advancing breeding strategies for quality improvement in S. miltiorrhiza.
Journal Article
Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic
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
Journal Article
The healthy house
A practical guide to creating healthy houses. What is a healthy, low-allergy house? What is a \"sick\" building? How is a healthy house designed and built? How can a sick environment be made healthier? These are some of the questions answered in this book.
Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm
Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine regression (SVR) algorithm to establish a chlorophyll a (Chl-a) prediction model and conduct Chl-a sensitivity analysis. In 2018, the average Chl-a content was 126.25 ug/L. The maximum total nitrogen (TN) content was 16.68 mg/L and high year-round. The average NH
4
+
-N and total phosphorous (TP) contents were only 0.78 and 0.18 mg/L. The content of NH
4
+
-N was higher in spring and increased significantly along the water flow, while TP decreased slightly along the water flow. We used a radial basis function kernel SVR model and tenfold cross-validation method to optimize parameters. The penalty parameter
c
was 1.4142, the kernel function parameter
g
was 1, and the training and verification errors were only 0.032 and 0.067, respectively, indicating a good model fit. Based on a sensitivity analysis of the SVR prediction model, the maximum sensitivity coefficients of Chl-a to TP and WT were 0.571 and 0.394, respectively, and the contributions were 33% and 22%, respectively. The next highest sensitivity coefficients were those of DO (0.28, 16%) and pH (0.243, 14%). The sensitivity coefficients of TN and NH
4
+
-N were the lowest. According to the current water environment pollution conditions, TP is the limiting factor of Chl-a in the Qingshui River, and it is also the main prevention and control factor of phytoplankton outbreak.
Graphical Abstract
Journal Article