Environmental Science and Ecotechnology最新文献

China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.

Methane metabolism, driven by methanogenic and methanotrophic microorganisms, plays a pivotal role in the carbon cycle. As seawater intrusion and soil salinization rise due to global environmental shifts, understanding how salinity affects methane emissions, especially in deep strata, becomes imperative. Yet, insights into stratigraphic methane release under varying salinity conditions remain sparse. Here we investigate the effects of salinity on methane metabolism across terrestrial and coastal strata (15–40 m depth) through in situ and microcosm simulation studies. Coastal strata, exhibiting a salinity level five times greater than terrestrial strata, manifested a 12.05% decrease in total methane production, but a staggering 687.34% surge in methane oxidation, culminating in 146.31% diminished methane emissions. Salinity emerged as a significant factor shaping the methane-metabolizing microbial community's dynamics, impacting the methanogenic archaeal, methanotrophic archaeal, and methanotrophic bacterial communities by 16.53%, 27.25%, and 22.94%, respectively. Furthermore, microbial interactions influenced strata system methane metabolism. Metabolic pathway analyses suggested Atribacteria JS1's potential role in organic matter decomposition, facilitating methane production via Methanofastidiosales. This study thus offers a comprehensive lens to comprehend stratigraphic methane emission dynamics and the overarching factors modulating them.

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO₃⁻)). The mechanism between ε(NO₃⁻) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO₃⁻). Here we introduce a supervised machine learning approach—the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH₄⁺, SO₄²⁻, and temperature as pivotal drivers for ε(NO₃⁻). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH₄⁺ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Waterborne viral epidemics are a major threat to public health. Increasing interest in wastewater reclamation highlights the importance of understanding the health risks associated with potential microbial hazards, particularly for reused water in direct contact with humans. This study focused on identifying viral epidemic patterns in municipal wastewater reused for recreational applications based on long-term, spatially explicit global literature data during 2000–2021, and modelled human health risks from multiple exposure pathways using a well-established quantitative microbial risk assessment methodology. Global median viral loads in municipal wastewater ranged from 7.92 × 10⁴ to 1.4 × 10⁶ GC L⁻¹ in the following ascending order: human adenovirus (HAdV), norovirus (NoV) GII, enterovirus (EV), NoV GI, rotavirus (RV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Following secondary or tertiary wastewater treatment, NoV GI, NoV GII, EV, and RV showed a relatively higher and more stable log reduction value with medians all above 0.8 (84%), whereas SARS-CoV-2 and HAdV showed a relatively lower reduction, with medians ranging from 0.33 (53%) to 0.55 (72%). A subsequent disinfection process effectively enhanced viral removal to over 0.89-log (87%). The predicted event probability of virus-related gastrointestinal illness and acute febrile respiratory illnesses in reclaimed recreational water exceeded the World Health Organization recommended recreational risk benchmark (5% and 1.9%, respectively). Overall, our results provided insights on health risks associated with reusing wastewater for recreational purposes and highlighted the need for establishing a regulatory framework ensuring the safety management of reclaimed waters.

Wind energy has become one of the most important measures for China to achieve its carbon neutrality goal. The spatial and temporal evolvement of economic competitiveness for wind energy becomes an important concern in shaping the decarbonization pathway in China. There has been an urgent need in power system planning to model the future dynamics of cost decline and supply potential for wind power in the context of carbon neutrality until 2060. Existing studies often fail to capture the rapid decline in the cost of wind power generation in recent years, and the prediction of wind power cost decline is more conservative than the reality. This study constructs an integrated model to evaluate the cost-competitiveness and grid parity potential of China's onshore wind electricity at fine spatial resolution with updated parameters. Results indicate that the total onshore wind potential amounts to 54.0 PWh. The average levelized cost of wind power is expected to decline from CNY 0.39 kWh⁻¹ in 2020 to CNY 0.30 and CNY 0.21 kWh⁻¹ in 2030 and 2060. 28.3%, 67.6%, and 97.6% of the technical potentials hold power costs lower than coal power in 2020, 2030, and 2060.

Advanced oxidation processes (AOPs) utilizing persulfate (PS) offer great potential for wastewater treatment. Yet, the dependency on energy and chemical-intensive activation techniques, such as ultraviolet radiation and transition metal ions, constrains their widespread adoption. Recognizing this limitation, researchers are turning towards the piezoelectric effect—a novel, energy-efficient method for PS activation that capitalizes on the innate piezoelectric characteristics of materials. Intriguingly, this method taps into weak renewable mechanical forces omnipresent in nature, ranging from wind, tides, water flow, sound, and atmospheric forces. In this perspective, we delve into the burgeoning realm of piezoelectric/PS-AOPs, elucidating its fundamental principles, the refinement of piezoelectric materials, potential mechanical force sources, and pertinent application contexts. This emerging technology harbors significant potential as a pivotal element in wastewater pretreatment and may spearhead innovations in future water pollution control engineering.

Increasing energy demands and environmental pollution concerns press for sustainable and environmentally friendly technologies. Soil microbial fuel cell (SMFC) technology has great potential for carbon-neutral bioenergy generation and self-powered electrochemical bioremediation. In this study, an in-depth assessment on the effect of several carbon-based cathode materials on the electrochemical performance of SMFCs is provided for the first time. An innovative carbon nanofibers electrode doped with Fe (CNFFe) is used as cathode material in membrane-less SMFCs, and the performance of the resulting device is compared with SMFCs implementing either Pt-doped carbon cloth (PtC), carbon cloth, or graphite felt (GF) as the cathode. Electrochemical analyses are integrated with microbial analyses to assess the impact on both electrogenesis and microbial composition of the anodic and cathodic biofilm. The results show that CNFFe and PtC generate very stable performances, with a peak power density (with respect to the cathode geometric area) of 25.5 and 30.4 mW m⁻², respectively. The best electrochemical performance was obtained with GF, with a peak power density of 87.3 mW m⁻². Taxonomic profiling of the microbial communities revealed differences between anodic and cathodic communities. The anodes were predominantly enriched with Geobacter and Pseudomonas species, while cathodic communities were dominated by hydrogen-producing and hydrogenotrophic bacteria, indicating H₂ cycling as a possible electron transfer mechanism. The presence of nitrate-reducing bacteria, combined with the results of cyclic voltammograms, suggests microbial nitrate reduction occurred on GF cathodes. The results of this study can contribute to the development of effective SMFC design strategies for field implementation.