Diatoms and dinoflagellates are two typical functional groups of phytoplankton assemblages, which play a crucial role in the structure and functioning of most marine ecosystems. To date, a novel challenge in ecology and biogeochemistry is to address the influences of environmental changes associated with climate change and human activities on the dynamics of diatoms and dinoflagellates. However, the knowledge of the key environmental factors controlling the diatom-dinoflagellate dynamics remains to be improved, particularly in the coastal ecosystems. Therefore, we conducted four cruises along the Qingdao coastline in spring, summer, autumn, and winter 2022 to explore how diatoms and dinoflagellates varied in response to regional environmental changes. The results showed that the phytoplankton communities were dominated by diatoms and dinoflagellates in terms of abundance and species diversity throughout the year in the study region. Yet, there were significant seasonal variability of diatoms and dinoflagellates across the four seasons. For example, diatom species was the most diverse during autumn, and the higher average abundance was observed in the fall and winter. In contrast, the average abundance of dinoflagellates was maximum during the summer and minimum in the autumn season. Moreover, the abundance and species ratios of diatoms/dinoflagellates (dia/dino) also showed significant seasonal variations in the region. The dia/dino abundance ratio was lowest in summer, while the dia/dino species ratio showed an increasing trend from spring to fall and a slight descending trend during winter. Based on the redundancy analysis, we revealed that diatoms and dinoflagellates responded differently to various environmental variables in different seasons, of which temperature and nutrients (especially dissolved inorganic nitrogen, DIN) had highly significant correlations with both the dia/dino abundance and species ratios. Thus, we suggested that temperature and DIN were the key factors controlling the seasonal dynamics of diatoms and dinoflagellates in the Qingdao coastal area.

Future phosphorus (P) shortages could seriously affect terrestrial productivity and food security. We investigated the changes in topsoil available P (AP) and total P (TP) in China\'s forests, grasslands, paddy fields, and upland croplands during the 1980s-2010s based on substantial repeated soil P measurements (63,220 samples in the 1980s, 2000s, and 2010s) and machine learning techniques. Between the 1980s and 2010s, total soil AP stock increased with a small but significant rate of 0.13 kg P ha-1  year-1 , but total soil TP stock declined substantially (4.5 kg P ha-1  year-1 ) in the four ecosystems. We quantified the P budgets of soil-plant systems by harmonizing P fluxes from various sources for this period. Matching trends of soil contents over the decades with P budgets and fluxes, we found that the P-surplus in cultivated soils (especially in upland croplands) might be overestimated due to the great soil TP pool compared to fertilization and the substantial soil P losses through plant uptake and water erosion that offset the P additions. Our findings of P-deficit in China raise the alarm on the sustainability of future biomass production (especially in forests), highlight the urgency of P recycling in croplands, and emphasize the critical role of country-level basic data in guiding sound policies to tackle the global P crises.

Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.

The control and monitoring of the environmental conditions in mushroom cultivation has been a challenge in the mushroom industry. Currently, research has been conducted to implement successful remote environmental monitoring, or, in some cases, remote environmental control, yet there is not yet a combination of both these systems providing live stream images or video. As a result, this research aimed to design and develop an Internet of things (IoT)-based environmental control and monitoring system for mushroom cultivation, whereby the growth conditions of the mushrooms, such as temperature, humidity, light intensity, and soil moisture level, are remotely monitored and controlled through a mobile and web application. Users would be able to visualize the growth of the mushroom remotely by video and images through the Internet. The respective sensors are implemented into the mushroom cultivation process and connected to the NodeMCU microcontroller, which collects and transfers the data to the cloud server, enabling remote access at any time through the end device with internet connection. The control algorithm regulates the equipment within the cultivational chamber autonomously, based on feedback from the sensors, in order to retain the optimum environment for the cultivation of mushrooms. The sensors were tested and compared with manual readings to ensure their accuracy. The implementation of IoT toward mushroom cultivation would greatly contribute to the advancement of the current mushroom industry which still applies the traditional cultivation approach.

Indoor environmental control is usually applied in poultry farming to ensure optimum growth conditions for birds. However, these control methods represent a considerable share of total energy consumption, and the trend of applying new equipment in the future for precision livestock farming would further increase energy demand, resulting in an increase in greenhouse gas emissions and management costs. Therefore, to ensure optimum efficiency of both energy use and livestock productivity, a customized hourly model was developed in the present study to interpret and analyze the electronically collected data. The modules for estimating indoor gas concentrations were incorporated into the present model, as this has not been properly considered in previous studies. A validation test was performed in a manure-belt layer house using sensors and meters to measure the indoor environmental parameters and energy consumption. The predicted results, including indoor temperature, relative humidity, carbon dioxide and ammonia concentrations, showed good agreement with the measured data, indicating a similar overall trend with acceptable discrepancies. Moreover, the corresponding differences between the measured and simulated energy consumption for heating, tunnel ventilation and base ventilation were 13.7, 7.5, and 0.1%, respectively. The total energy demand estimated by the model showed a limited discrepancy of approximately 10.6% compared with that measured in reality. Although human factors, including inspection, cleaning, vaccination, etc., were not included in the model, the validation results still suggested that the customized model was able to accurately predict the indoor environment and overall energy consumption during poultry farming. The validated model provides a tool for poultry producers to optimize production planning and management strategies, increase the production rate of unit energy consumption and achieve precision livestock farming from an energy consumption standpoint.

Regulating the distribution of reactive oxygen species generated from H2 O2 activation is the prerequisite to ensuring the efficient and safe use of H2 O2 in the chemistry and life science fields. Herein, we demonstrate that constructing a dual Cu-Fe site through the self-assembly of single-atomic-layered Cu5 nanoclusters onto a FeS2 surface achieves selective H2 O2 activation with high efficiency. Unlike its unitary Cu or Fe counterpart, the dual Cu-Fe sites residing at the perimeter zone of the Cu5 /FeS2 interface facilitate H2 O2 adsorption and barrierless decomposition into ⋅OH via forming a bridging Cu-O-O-Fe complex. The robust in situ formation of ⋅OH governed by this atomic-layered catalyst enables the effective oxidation of several refractory toxic pollutants across a broad pH range, including alachlor, sulfadimidine, p-nitrobenzoic acid, p-chlorophenol, p-chloronitrobenzene. This work highlights the concept of building a dual catalytic site in manipulating selective H2 O2 activation on the surface molecular level towards efficient environmental control and beyond.

The recent pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has increased the global concern over its transmission and prevention. Since there was an incontrovertible perception that environmental conditions might affect the spread of SARS-CoV-2, we analyzed the environmental variables impact on SARS-CoV-2 transmission in Pakistan. Our analytical study revealed that when the temperature remained high for consecutive 10 to 15 days, a significant decrease is witnessed in the forthcoming week in the COVID-19 cases. We found that a persistent high ambient temperature of 36 °C and above is detrimental to SARS-CoV-2 transmission and has paused the community spread of COVID-19 in Pakistan. Importantly, during the fall period of SARS-CoV-2, a high relative humidity of 63-72% was noted that showed a positive role in COVID-19 inhibition. Hence, the community spread of SARS-CoV-2 was paused by the warm humid climate and the ambient temperature was the yardstick in turning down the epidemic in Pakistan.

An appealing strategy in the direction of circular chemistry and sustainable nitrogen exploitation is to efficiently convert NOx pollutants into low-toxic products and simultaneously provide crop plants with metabolic nitrogen. This study demonstrates that such a scenario can be realized by a defect- and morphology-coengineered Ni-Fe-layered double hydroxide (NiFe-LDH) comprising ultrathin nanosheets. Rich oxygen vacancies are introduced onto the NiFe-LDH surface, which facilitate charge carrier transfer and enable photocatalytic O2 activation into superoxide radicals (•O2-) under visible light. •O2- on NiFe-LDH thermodynamically oxidizes NO into nitrate with selectivity over 92%, thus suppressing dangerous NO2 emissions. By merit of abundant mesopores on NiFe-LDH ultrathin nanosheets bearing a high surface area (103.08 m2/g), nitrate can be readily stored without compromising the NO oxidation reactivity or selectivity for long-term usage. The nitrate species can be easily washed off the NiFe-LDH surface and then enriched in the liquid form as easy-to-use chemicals.

The present study investigated the predictive effects of clusters of variables on homework environment management based on the data from 3018 students in Grade 8. These clusters included: background variables, homework characteristics, adult support and monitoring, homework purposes, goal orientations, and contextual control. At the individual level, management of homework environment was significantly related to at least one variable from each of the six clusters. Specifically, it was associated negatively with time spent watching TV, and positively with prior achievement, homework interest, homework quality, family help, teacher feedback, academic purpose, self-regulatory purpose, mastery-approach, and help seeking. Additionally, males managed homework environment less frequently than females. Finally, management of homework environment was positively related to homework quality at the class level.
BACKGROUND: The online version contains supplementary material available at 10.1007/s12144-021-02596-5.

Removal of non-biodegradable heavy metals has been the top priority in wastewater treatment and the development of green technologies remains a significant challenge. We demonstrate that phosphorylated nanoscale zero-valent iron (nZVI) is promising for removal of heavy metals (NiII , CuII , CrVI , HgII ) via a boosted Kirkendall effect. Phosphorylation confines tensile hoop stress on the nZVI particles and \"breaks\" the structurally dense spherical nZVI to produce numerous radial nanocracks. Exemplified by NiII removal, the radial nanocracks favor the facile inward diffusion of NiII and the rapid outward transport of electrons and ferrous ions through the oxide shell for surface (NiII /electron) and boundary (NiII /Fe0 ) galvanic exchange. Accompanied by a pronounced hollowing phenomenon, phosphorylated nZVI can instantly reduce and immobilize NiII throughout the oxide shell with a high capacity (258 mg Ni g-1  Fe). For real electroplating factory wastewater treatment, this novel nZVI performs simultaneous NiII and CuII removal, producing effluent of stable quality that meets local discharge regulations.