To reduce the losses caused by the atmospheric corrosion of carbon steels, it is important to establish a prediction model to determine the corrosion rate of carbon steels in natural environments. In this study, a prediction model of atmospheric corrosion of Q235 carbon steel (PMACC-Q235) in China was established by coupling the mean impact value algorithm and back propagation artificial neural network. Tempo-spatial patterns of corrosion rates in five long-exposure time categories across China were analyzed. Ten main factors affecting the atmospheric corrosion of Q235 were identified. The corrosion rates in a single year were similar (approximately 30 μm/a) and larger than those for 2 (25.30 μm/a) and 3 years (21.66 μm/a). The spatial corrosion rates in the northwestern areas were primarily lower than those in southeastern coastal areas. This could be influenced by climatic factors, such as temperature, humidity, and precipitation. All corrosion rates reached the C2 level (>1.3 μm/a), and there was some possibility that they reached higher corrosion levels. The largest probability for the C3 level in all periods was an average of 0.91, and that for the C4 level was 0.83. Spatially, higher probabilities were mainly located in the southern area, especially in Hainan, located in the south and surrounded by sea. Corrosion rates largely varied among climatic zones, and mean corrosion rates in the tropical monsoon climate zone were the largest (average of three periods 33.39 μm/a). SO₂ and soluble-dust fall had the largest impact on the variations in the corrosion rates among different climatic zones.

The European Union (EU) Emissions Trading System is the most important means for the EU to achieve carbon neutrality, but it has been severely affected by the outbreak of COVID-19 in Europe, and carbon price have fluctuated sharply. Research on the driving factors of carbon price during this period will help maintain the stability of the carbon emissions trading market and promote the realization of carbon neutrality. This study selected the EU carbon allowance futures price as the research object and applied the Bai–Perron structural break test to analyze the factors that influences carbon price fluctuations using the Johansen cointegration technique and the Newey–West regression estimation. Studies have shown that the outbreak of COVID-19 and the “€750 billion green recovery plan” both had a significant impact on EU carbon price. Carbon price has also undergone significant structural changes. Under the influence of these two factors, the relationship between the level of economic development and carbon price displayed a short-term negative correlation. At the same time, oil price and interbank dismantling rates were also important factors affecting carbon price, while the impact of the clean development mechanism on carbon price was not significant. The study confirmed the effectiveness of the EU’s “green recovery plan” in stabilizing the carbon market during the COVID-19 pandemic and will provide a reference for the formulation of economic recovery policies of countries around the world.

IoT plays an important role in the overall development and advancement of the country as it is the key ingredient for the development of the smart environment. IoT is a network of physical objects, devices that contain embedded technologies such as sensors, controllers, etc., which can sense, communicate, and interact with the system to carry out desired operations. The advancement in technology over the past years has provided a new era for computational processing and sensing to facilitate the vision of a smart environment. Researchers have put several efforts to use IoT to facilitate our lives. This paper purposes on an integrated smart environment using IoT. Various sectors such as agriculture, transportation, garbage collection, security issues, sensors, etc. are discussed along with the key technologies including RFID, IP, EPC, Wi-Fi, Bluetooth, and ZigBee. This paper will provide a complete insight into the one who wants to research in the field of IoT. It also highlights the unprecedented opportunities brought by IoT-based technologies to human life. Finally, we have discussed the future enhancements in IoT.

This study aimed to test the efficiency of ethylene (Eth; 200 µL L⁻¹ ethephon) in presence or absence of nitrogen (N; 80 mg N kg⁻¹ soil) in protecting photosynthetic apparatus from copper (Cu; 100 mg Cu kg⁻¹ soil) stress in mustard (Brassica juncea L.) and to elucidate the physio-biochemical modulation for Eth plus N-induced Cu tolerance. Elevated Cu-accrued reductions in photosynthesis and growth were accompanied by significantly higher Cu accumulation in leaves and oxidative stress with reduced assimilation of N and sulfur (S). Ethylene in coordination with N considerably reduced Cu accumulation, lowered lipid peroxidation, lignin accumulation, and contents of reactive oxygen species (hydrogen peroxide, H₂O₂, and superoxide anion, O₂•⁻), and mitigated the negative effect of Cu on N and S assimilation, accumulation of non-protein thiols and phytochelatins, enzymatic, and non-enzymatic antioxidants (activity of ascorbate peroxidase, APX, and glutathione reductase, GR; content of reduced glutathione, GSH, and ascorbate, AsA), cell viability, photosynthesis, and growth. Overall, the effect of ethylene-nitrogen synergism was evident on prominently mitigating Cu stress and protecting photosynthesis. The approach of supplementing ethylene with N may be used as a potential tool to restrain Cu stress, and protect photosynthesis and growth of mustard plants.

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China’s steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China’s steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

In the development concern, all countries are started increasing production of energy across the world. All countries have started expansion of access to electricity across the nation. As a result, their economic growth significantly progress by increasing the share of access to electricity (energy use). Hence, the aim of this research is to examine the impact of access to electricity on economic development across five emerging countries, spanning the period 1990–2018 and by using the panel modelling methodology. The results of long-run elasticities reveal that access to electricity play a considerable role in promoting economic development across five emerging countries. Furthermore, the results on panel causality tests show the presence of unidirectional causality running from economic development to access to electricity in the short run. However, the study also estimates long-run elasticities for individual economies. This individual country empirical result also shows that access to electricity has a substantial positive impact on economic development for each of the countries. Finally, the empirical findings suggest that governments should act effectively in providing access to electricity for higher economic development in these countries.

In order to reduce the dust pollution caused by the coal mining process, a novel composite environmental dust suppressant for coal dust control was synthesized by corn straw, sodium carboxymethyl cellulose (CMC), and additives. This study focused on the preparation conditions of the dust suppressant, and the performances of which were investigated systematically. Response surface method (RSM) was used to optimize the raw material formulation and preparation parameters. The optimum mass ratio of straw, CMC, and alkali of the dust suppression was 65:20:15 (m/m), which was prepared under the conditions of the reaction time being 1.5 h and the rotation speed being 300 r/min. The pH of the dust suppressant was 8.0, and the state of which was suspension. Additives were benefited to enhance the suppressant performance, and the surface tension and the contact angle could decrease to 32.4 mN/m and 32.0°. The suppressant has a maximum viscosity of 363.6 mPa·s, and the compressive strength could be up to 200 kPa. The hygroscopic rate could reach more than 4%. The wind erosion resistance could be up to 99 % at the wind speed of 14 m/s. After spraying the dust suppressant, the gap between particles was filled with dust suppressant, and the adjacent particles were bound by strong mechanical action.

Climate change has remarkably altered growing-season vegetation growth, but the impacts of vegetation variability on the regional hydrological cycle remain poorly understood. Exploring the relationships between climate change, vegetation dynamics, and hydrologic factors would contribute to the sustainable management of ecosystems. Here, we investigated the response of vegetation dynamics to climate change and its impact on hydrologic factors in a traditional agricultural basin with limited water resources in China, Nansi Lake Basin (NLB). To this end, CASA (Carnegie-Ames-Stanford Approach) model and the SWAT (Soil and Water Assessment Tool) model were applied to simulate the net primary productivity (NPP), evapotranspiration (ET), and soil water in the growing season (April–October) from 2000 to 2016. Results showed that the mean growing-season NPP (NPPGS) exhibited an ascending trend at a rate of 2.93 g C/m²/year during the 17-year period. The intra-annual variation of NPPGS displayed two peaks in May and July, respectively. The first peak in May was accompanied by relative deficits in soil water, which might inhibit vegetation productivity. Precipitation was the principal climatic factor controlling NPPGS dynamics in the water-limited NLB. The positive influence of temperature on NPPGS was relatively weak, and even future warming could negatively affect ecosystem productivity in the south-central regions of the NLB. Furthermore, a strongly positive relationship between NPPGS and ET was detected, suggesting that increasing NPP in the future might stimulate the rise in ET and then exacerbate drought at the watershed scale. This study provides an integrated model for a comprehensive understanding of the interaction between vegetation, climate, and hydrological cycle, and highlights the importance of water-saving agriculture for future food security.

Since the modernisation of the agricultural sector in Morocco, the Triffa plain has been experiencing the development of techniques and increased production. However, agricultural intensification, the uncontrolled use of fertilisers and pesticides, and the succession of years of drought have contributed to the overexploitation of water resources and the degradation of the groundwater quality in the plain. To study the impact of the poor quality of groundwater (nitric pollution and salinisation) on the health of vegetation, we examined the spatiotemporal evolution of nitrates and salinisation in the plain, as well as the chlorophyll activity of crops (NDVI) using the Google Earth Engine (GEE) associated with MOD13Q1 version 6 images, for the years 2007-2009-2016-2022. The main results show an increase of 174% of the chlorophyll activity (NDVI ≥ 0.6) between 2007 and 2022, which make evidence of the significant intensification of the agricultural sector in the plain accompanied by a fast spread of nitric pollution regarding the groundwater. Hence, the area covered by the poor to very poor groundwater quality has almost doubled in a time interval of years ([Formula: see text] = 235.5 km², [Formula: see text] = 408.65 km²). According to the analysis of the normalised difference vegetation index maps calculated for the study periods: 2007-2009-2016-2022 and the Pearson correlation matrix between the different variables, we conclude that the degradation of groundwater quality negatively affects the chlorophyll activity, which will consequently have an impact on the crop yield. The results prove the excessive use of fertilisers and pesticides, which led to increased chlorophyll activity associated with fast degradation of the water’s quality concerning nitrate concentrations.

PM₂.₅ poses a threat to human health. It is important to evaluate the potential risk of PM₂.₅ inhalation exposure when people are located in different spatiotemporal activity locations. In this study, the PM₂.₅ concentration was detected by the atmospheric cruise monitoring system (ACMS), a new detection technology used for city-wide PM₂.₅ concentration monitoring. People were divided into eight categories of five typical activity patterns, including rest (R), sedentary behavior (SB), light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA). The PM₂.₅ inhalation exposure risk was then estimated for these typical activities. The research results showed that the time sequence data of the ACMS had a similar tendency to change as those of the traditional air quality monitoring stations (AQMS). Although both passed the stationarity test, the relative error (RE) of the monthly average PM₂.₅ concentration between the ACMS and AQMS was 7.5–14%. RE was usually lower when the individual air quality index (IAQI) of PM₂.₅ was higher. Otherwise, RE was higher. The research results also showed that PM₂.₅ exposure was positively correlated with PM₂.₅ concentration, respiration rate, and human activity patterns. Because adults had a higher monthly average potential exposure (MAPE) than minors and that males had a higher MAPE than females. The potential exposure generated by LPA and MPA reached 50.76% of the total potential exposure (TPE). VPA brought about a 14.7% increase in the TPE. The research findings are helpful to understand the temporal distribution characteristics of PM₂.₅ concentrations and guide the potential risk evaluation of PM₂.₅ inhalation exposure.