Potential for future reductions of global GHG and air pollutants from circular waste management systems.
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ABSTRACT:
SUBMITTER: Gomez-Sanabria A
PROVIDER: S-EPMC8748894 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The circular economy (CE) is reasoned to organize complex systems supporting sustainable resilience by distinguishing between waste materials and economic growth. This is crucial to the electronic waste (e-waste) industry of developed countries, and e-waste operation management has become their top priority because e-waste contains toxic materials and valuable sources of elements. In the UK, although London Metropolitan city boasts an ambitious sustainable resilience target underlying the context of CE, practical implementation has yet to be feasible, with few investigations detailing if and how the existing target implications enable industrial and social-ecological sectors to continue their performance functionalities in the face of undesired disruptions. In this paper, a dynamic Bayesian Network (dynamic BN) approach is developed to address a range of potential risks. The existing London e-waste operation management is considered as an application of study for sustainable resilience development. Through the utilization of dynamic BN, a comprehensive analysis yields a Resilience Index (RI) of 0.5424, coupled with a StdDev of 0.01350. These metrics offer a profound insight into the intricate workings of a sustainable system and its capacity to swiftly rebound from unexpected shocks and disturbances. This newfound understanding equips policymakers with the knowledge needed to navigate the complexities of sustainable e-waste management effectively. The implications drawn from these in-depth analyses furnish policymakers with invaluable information, enabling them to make judicious decisions that advance the cause of sustainable e-waste management. The findings underscore that the absorptive capacity of a sustainable and resilient e-waste operation management system stands as the foremost defense mechanism against unforeseen challenges. Furthermore, it becomes evident that two pivotal factors, namely "diversifying the supply chain" and "enhancing supply chain transparency," play pivotal roles in augmenting the sustainability and resilience of e-waste operation management within the context of London's ambitious sustainability targets. These factors are instrumental in steering the trajectory of e-waste management towards a more sustainable and resilient future, aligning with London's aspirations for a greener and more eco-conscious future.
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Project description:With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.
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Project description:BackgroundThe potential contributions from forest-based greenhouse gas (GHG) mitigation actions need to be quantified to develop pathways towards net negative emissions. Here we present results from a comparative analysis that examined mitigation options for British Columbia's forest sector. Mitigation scenarios were evaluated using a systems perspective that takes into account the changes in emissions and removals in forest ecosystems, in harvested wood product (HWP) carbon stocks, and in other sectors where wood products substitute for emission-intensive materials and fossil fuels. All mitigation activities were assessed relative to a forward-looking 'business as usual' baseline for three implementation levels. In addition to quantifying net GHG emission reductions, we assessed economic, and socio-economic impacts as well as other environmental indicators relating to forest species, age class, deadwood availability and future timber supply. We further considered risks of reversal for land-based scenarios, by assessing impacts of increasing future wildfires on stands that were not harvested.ResultsOur spatially explicit analyses of forest sector mitigation options demonstrated a cost-effective portfolio of regionally differentiated scenarios that directed more of the harvested wood to longer-lived wood products, stopped burning of harvest residues and instead produced bioenergy to displace fossil fuel burning, and reduced harvest levels in regions with low disturbance rates. Domestically, net GHG emissions were reduced by an average of -9 MtCO2e year-1 over 2020-2050 for a portfolio of mitigation activities at a default implementation level, with about 85% of the GHG emission reductions achieved below a cost of $50/tCO2e. Normalizing the net GHG reduction by changes in harvested wood levels permitted comparisons of the scenarios with different ambition levels, and showed that a 1 MtCO2 increase in cumulative harvested stemwood results in a 1 MtCO2e reduction in cumulative emissions, relative to the baseline, for the Higher Recovery scenario in 2070.ConclusionsThe analyses conducted in this study contribute to the global understanding of forest sector mitigation options by providing an integrated framework to synthesize the methods, assumptions, datasets and models needed to quantify mitigation activities using a systems approach. An understanding of economically feasible and socio-economically attractive mitigation scenarios along with trade offs for environmental indicators relating to species composition and age, helps decision makers with long-term planning for land sector contributions to GHG emission reduction efforts, and provides valuable information for stakeholder consultations.
| S-EPMC7531139 | biostudies-literature
Project description:Waste to energy (WTE) technologies have emerged as an alternative solution to municipal solid waste management. WTE systems provide major environmental and economic benefits by converting waste into accessible energy, as part of an integrated solid waste management (ISWM) strategy. However, previous studies showed that establishing an ISWM strategy based on a single type of WTE systems does not necessarily realize maximum benefits. Hence, optimizing the selection of WTE systems as part of a hybrid waste management strategy can potentially achieve maximum benefits and minimize negative impacts. However, such task is challenging due to the various alternatives and objectives, particularly those related to the material and energy recovery systems. This article presents the methods used to develop a systematic optimization framework that identifies the most beneficial set of ISWM systems through mathematical modelling. The methods include the procedures of the established framework, including base model computations, as well as the comprehensive modelling and optimization methods.•The energy recovery, carbon footprint, and financial profitability are computed for selected WTE facilities.•The multi-objective mathematical programming is solved using the weighted comprehensive criterion method (WCCM).•The model is implemented in CPLEX software using mathematical programming language (OPL).
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Project description:The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.
| S-EPMC8372483 | biostudies-literature
Project description:BackgroundEstimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.MethodsWe conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).ResultsNO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.ConclusionsNO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.
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Project description:Globally, industrialisation and urbanisation have led to the generation of hazardous waste (HW). Sustainable hazardous waste management (HWM) is the need of the hour for a safe, clean, and eco-friendly environment and public health. The prominent waste management strategies should be aligned with circular economic models considering the economy, environment, and efficiency. This review critically discusses HW generation and sustainable management with the strategies of prevention, reduction, recycling, waste-to-energy, advanced treatment technology, and proper disposal. In this regard, the major HW policies, legislations, and international conventions related to HWM are summarised. The global generation and composition of hazardous industrial, household, and e-waste are analysed, along with their environmental and health impacts. The paper critically discusses recently adapted management strategies, waste-to-energy conversion techniques, treatment technologies, and their suitability, advantages, and limitations. A roadmap for future research focused on the components of the circular economy model is proposed, and the waste management challenges are discussed. This review stems to give a holistic and broader picture of global waste generation (from many sources), its effects on public health and the environment, and the need for a sustainable HWM approach towards the circular economy. The in-depth analysis presented in this work will help build cost-effective and eco-sustainable HWM projects.
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