The evaluation of greenhouse gas emissions from sewage treatment with urbanization: Understanding the opportunities and challenges for climate change mitigation in China's low-carbon pilot city, Shenzhen

Navigating the heights of environmental impacts of the Himalayan waste management system through life cycle assessment approach

The Himalayan region, characterized by its unique ecological diversity and fragility, faces escalating challenges related to waste management against the backdrop of global concerns about climate change. Rapid urbanization, population growth, changing consumption patterns, and thriving tourism have intensified the generation of municipal solid waste, contributing to the release of GHGs. This study aimed to quantify GHG emissions associated with waste management practices in the region. LCA was employed to evaluate the environmental impacts of waste management practices, identifying key areas for improvement and sustainable solutions. Contribution of waste management practices of composting, material recovery facilities, waste-to-energy, RDF facilities, landfills, incineration, and waste transportation were assessed in the state of Himachal Pradesh. The municipal solid waste management infrastructures in the state contributed to 3,98,098 tCO2eqyr-1 emissions of which waste transportation and landfills were identified as the major sources, highlighting the constraint of infrastructure in rural areas of the region. They made up 82% of all the emissions from waste management infrastructures in the state. The LCA studies confirmed that landfills for MSW were the major source of environmental incompatibility in the state. However, material recovery and fuel production practices in MSW management facilities drastically reduced the impacts on indicators, namely, abiotic depletion, acidification, freshwater aquatic ecotoxicity, human toxicity, and ozone depletion potential. The findings highlight the pressing need for efficient waste management facilities in the state to bolster climate change resilience and environmental compatibility, given the current inadequacies in infrastructure, processes, and skilled manpower.

Improving real-time forecasting of bay water quality by integrating in-situ monitoring, machining learning, and process-based modeling

Frequent occurrences of disasters such as red tides significantly threaten bay ecosystems, making near real-time water quality forecasting crucial for disaster warning and decision-making. Conventional techniques, such as process-based modeling and in-situ monitoring, struggle to achieve this for the entire bay region when applied independently. This study proposes a hybrid approach that integrates in-situ monitoring, process-based modeling, and machine learning (ML) to address this challenge. The feasibility of the approach was validated using Shenzhen Bay, a cross-boundary bay co-administered by Shenzhen and Hong Kong, as a testbed. ML models exhibited superior performance for location-specific forecasting, achieving Nash-Sutcliffe efficiency (NSE) values of 0.90, 0.84, 0.85, and 0.73 for dissolved oxygen, chlorophyll a, total nitrogen, and total phosphorus, respectively. Forecasting accuracy declined with longer lead times. Additionally, this study developed a dual-clustering method to optimize the selection of monitoring locations, minimizing the number of sites while effectively capturing the water quality across the entire bay. The results suggest that a monitoring network consisting of just two locations can adequately represent the overall water quality conditions within Shenzhen Bay. Using output from the Delft-3D model built for Shenzhen Bay, ML-based surrogate models successfully extended water quality forecasting from the two strategically selected locations to the entire bay area, with NSE values exceeding 0.8 in most regions. The hybrid approach provides a methodological foundation for achieving near real-time water quality forecasting across the entire bay areas, contributing to maintaining water security and preserving valuable ecosystem services in a rapidly changing environment.

A review-based estimation of GHG emissions of China's wastewater management system

Under China's "Dual Carbon Goal", the wastewater treatment system plays a crucial role in the country's efforts to reduce greenhouse gas (GHG) emissions. However, a lack of baseline emissions data poses challenges for decarbonization efforts. This study aims to profile and diagnose the GHG emissions of China's entire wastewater system and identify key contributing factors. Our findings show that China's wastewater system, including wastewater treatment plants (WWTPs) and septic tanks, is responsible for significant emissions, with baseline estimates at 108.26 ± 47.37 Mt CO2-eq/a. Septic tanks and WWTPs emerged as the major GHG hotspots, contributing the most to the total emissions. This study highlights the variability in emission results from previous literature, stressing the need for consistent accounting methods and scientific emission factors. Additionally, current on-site monitoring practices in China show gaps, which hinder the accurate determination of baseline emissions. To guide future emission reduction strategies, regulatory frameworks and improved monitoring practices are recommended for the wastewater sector in China.

Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model

With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters. This work pays particular attention to their impact on key indicators such as Chemical Oxygen Demand (COD), NH4+-N, Total Phosphorus (TP), and Total Nitrogen (TN), and the application of predictive models. The work first establishes a Random Forest Regression (RFR) model. The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model's prediction accuracy and stability. Using bootstrap sampling, the RFR model generates multiple training subsets from the original data and randomly selects subsets of variables to construct regression trees. Its performance in predicting various water quality indicators is then evaluated. The results show that the RFR model exhibits excellent performance, achieving high levels of prediction accuracy and stability for all indicators. For example, the R2 for COD prediction is 0.99954, while the R2 values for NH4+-N, TP, and TN predictions reach 0.99989. Compared to five other models, the RFR model demonstrates the best performance across all water quality indicator predictions. This work provides critical support for optimizing wastewater treatment technologies and developing water resource management policies. These findings also offer essential theoretical and empirical insights for the future improvement of urban wastewater treatment technologies and water resource management decision-making.

Investigating the environmental dynamics of emerging pollutants in response to global climate change: Insights from bibliometrics-based visualization analysis

The environmental dynamics of emerging pollutants were profoundly influenced by global climate change, attracting widespread attention to this complex interaction. However, single studies or reviews were insufficient to grasp, clarify, and predict the evolutionary characteristics and coupling patterns of emerging pollutants under global climate change. Here, 2389 research articles collected from the Web of Science Core Collection database for the period 2000-2023 were analyzed using systematic bibliometric visual analysis software. Results suggested a rapid growth trend in this field study, particularly accelerating after 2015. The United States, China, the United Kingdom, and Spain led in the volume of publications, forming a multidisciplinary research network centered on environmental science. Wastewater treatment, personal care products, pharmaceuticals, and heavy metals were identified as current research hotspots, with climate change emerging as the most prominent keyword. Research focus gradually shifted from single pollutants to multi-pollutant composite effects, from local issues to global-scale assessments, and from phenomenon description to mechanism analysis and risk evaluation. It is concluded that climate change is reshaping the environmental behaviors and ecological risks of emerging pollutants, and multidisciplinary, multi-scale research methods are urgent need. Future research is suggested to strengthen interdisciplinary collaboration, integrate climate and pollutant migration models, and investigate impacts of extreme climate events in depth.

Ensuring carbon neutrality via algae-based wastewater treatment systems: Progress and future perspectives

The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients-including carbon, nitrogen, and phosphorus-alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms.

Assessing greenhouse gas emissions from the printing and dyeing wastewater treatment and reuse system: Potential pathways towards carbon neutrality

The urgency of achieving carbon neutrality needs a reduction in greenhouse gas (GHG) emissions from the textile industry. Printing and dyeing wastewater (PDWW) plays a crucial role in the textile industry. The incomplete assessment of GHG emissions from PDWW impedes the attainment of carbon neutrality. Here, we firstly introduced a more standardized and systematic life-cycle GHG emission accounting method for printing and dyeing wastewater treatment and reuse system (PDWTRS) and proposed possible low-carbon pathways to achieve carbon neutrality. Utilizing case-specific operational data over 12 months, the study revealed that the PDWTRS generated 3.49 kg CO2eq/m3 or 1.58 kg CO2eq/kg CODrem in 2022. This exceeded the GHG intensity of municipal wastewater treatment (ranged from 0.58 to 1.14 kg CO2eq/m3). The primary contributor to GHG emissions was energy consumption (33 %), with the energy mix (sensitivity = 0.38) and consumption (sensitivity = 0.33) exerting the most significant impact on GHG emission intensity respectively. Employing prospective life cycle assessment (LCA), our study explored the potential of the anaerobic membrane bioreactor (AnMBR) to reduce emissions by 0.54 kg CO2eq/m3 and the solar-driven photocatalytic membrane reactor (PMR) to decrease by 0.20 kg CO2eq/m3 by 2050. Our projections suggested that the PDWTRS could achieve net-zero emissions before 2040 through an adoption of progressive transition to low-carbon management, with a GHG emission intensity of -0.10 kg CO2eq/m3 by 2050. Importantly, the study underscored the escalating significance of developing sustainable technologies for reclaimed water production amid water scarcity and climate change. The study may serve as a reminder of the critical role of PDWW treatment in carbon reduction within the textile industry and provides a roadmap for potential pathways towards carbon neutrality for PDWTRS.

An assessment on the new impetus of green energy development and its impact on climate change: a non-linear perspective

The purpose of this article is to investigate the new driving forces behind China's green energy and further assess the impact of green energy on climate change. The existing literature has used linear methods to investigate green energy, ignoring the non-linear relationships between economic variables. The nonparametric models can accurately simulate nonlinear relationships between economic variables. This paper constructs a nonparametric additive model and uses it to explore green energy. The empirical results show that the impact of green finance on green energy is more prominent in the later stage (a U-shaped impact). Fiscal decentralization also exerts a positive U-shaped impact, meaning that expanding local fiscal autonomy has contributed to green energy growth in the later stage. Similarly, the impact of oil prices and foreign direct investment demonstrates a positive U-shaped pattern. However, the nonlinear impact of environmental pressure displays an inverted U-shaped pattern. Furthermore, this article explores the impact of green energy on climate change and its impact mechanisms. The results exhibit green energy generates a positive U-shaped impact on climate change, meaning that the role of green energy in mitigating climate change gradually becomes prominent over time. Mechanism analysis exhibits that industrial structure and energy structure both produce a nonlinear influence on climate change.

High-resolution emission inventory of biogenic volatile organic compounds for rapidly urbanizing areas: A case of Shenzhen megacity, China

The effects of volatile organic compounds on urban air quality and the ozone have been widely acknowledged, and the contributions of relevant biogenic sources are currently receiving rising attentions. However, inventories of biogenic volatile organic compounds (BVOCs) are in fact limited for the environmental management of megacities. In this study, we provided an estimation of BVOC emissions and their spatial characteristics in a typical urbanized area, Shenzhen megacity, China, based on an in-depth vegetation investigation and using remote sensing data. The total BVOC emission in Shenzhen in 2019 was estimated to be 3.84 × 109 g C, of which isoprene contributed to about 24.4%, monoterpenes about 44.4%, sesquiterpenes about 1.9%, and other VOCs (OVOCs) about 29.3%. Metropolitan BVOC emissions exhibited a seasonal pattern with a peak in July and a decline in January. They were mainly derived from the less built-up areas (88.9% of BVOC emissions). Estimated BVOCs comprised around 5.2% of the total municipal VOC emissions in 2019. This percentage may increase as more green spaces emerge and anthropogenic emissions decrease in built-up areas. Furthermore, synergistic effects existed between BVOC emissions and relevant vegetation-based ecosystem services (e.g., air purification, carbon fixation). Greening during urban sprawl should be based on a trade-off between BVOC emissions and ecosystem benefits of urban green spaces. The results suggested that urban greening in Shenzhen, and like other cities as well, need to account for BVOC contributions to ozone. Meanwhile, greening cites should adopt proactive environmental management by using plant species with low BVOC emissions to maintain urban ecosystem services while avoid further degradation to ozone pollution.

The Association Between Urbanization and Electrocardiogram Abnormalities in China: a Nationwide Longitudinal Study

The health effects of urbanization are controversial. The association between urbanization and reversible subclinical risks of cardiovascular diseases (e.g., electrocardiogram (ECG) abnormalities) has rarely been studied. This study aimed to assess the association between urbanization and ECG abnormalities in China based on the China National Stroke Screening Survey (CNSSS). We used changes in the satellite-measured impervious surfaces rate and nighttime light data to assess the level of urbanization. Every interquartile increment in the impervious surfaces rate or nighttime light was related to a decreased risk of ECG abnormalities, with odds ratios of 0.894 (95% CI, 0.869-0.920) or 0.809 (95% CI, 0.772-0.847), respectively. And we observed a U-shaped nonlinear exposure-response relationship curve between the impervious surfaces rate and ECG abnormalities. In conclusion, the current average level of urbanization among the studied Chinese adults remains a beneficial factor for reducing cardiovascular risks.