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abstract An up-to-date assessment of environmental emissions in the US health care sector is essential to help policy makers hold the health care industry accountable to protect public health. We update nationallevel US health-sector emissions. We also estimate state-level emissions for the first time and examine associations with state-level energy systems and health care quality and access metrics. Economywide modeling showed that US health care greenhouse gas emissions rose 6 percent from 2010 to 2018, reaching 1,692 kg per capita in 2018-the highest rate among industrialized nations. In 2018 greenhouse gas and toxic air pollutant emissions resulted in the loss of 388,000 disability-adjusted life-years. There was considerable variation in state-level greenhouse gas emissions per capita, which were not highly correlated with health system quality. These results suggest that the health care sector's outsize environmental footprint can be reduced without compromising quality. To reduce harmful emissions, the health care sector should decrease unnecessary consumption of resources, decarbonize power generation, and invest in preventive care. This will likely require mandatory reporting, benchmarking, and regulated accountability of health care organizations.

Biologically based wastewater treatment systems are considered a sustainable, cost-effective alternative to conventional wastewater treatment systems. These systems have been used and studied for the treatment of urban sewage from small communities, and recently, it has been reported that they can also effectively remove emerging organic contaminants (EOCs). EOCs are a new group of unregulated contaminants which include pharmaceutical and personal care products, some pesticides, veterinary products, and industrial compounds among others that are thought to have long-term adverse effects on human health and ecosystems. This review is focused on reporting the ability of biologically based wastewater treatment systems to remove EOCs and the main elimination mechanisms and degradation processes (i.e., biodegradation, photodegradation, phytoremediation, and sorption) taking place in constructed wetlands, ponds, and Daphnia and fungal reactors.

Background An increasing number of systematic reviews (SRs) in the environmental field have been published in recent years as a result of the global concern about the health impacts of air pollution and temperature. However, no study has assessed and compared the methodological and reporting quality of SRs on the health effects of air pollutants and extreme temperatures. This study aims to assess and compare the methodological and reporting quality of SRs on the health effects of ambient air pollutants and extreme temperatures. Methods PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science, and Epistemonikos databases were searched. Two researchers screened the literature and extracted information independently. The methodological quality of the SRs was assessed through A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). The reporting quality was assessed through Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA). Results We identified 405 SRs (286 for air pollution, 108 for temperature, and 11 for the synergistic effects). The methodological and reporting quality of the included SRs were suboptimal, with major deficiencies in protocol registration. The methodological quality of SRs of air pollutants was better than that of temperature, especially in terms of satisfactory explanations for any heterogeneity (69.6% v. 45.4%). The reporting quality of SRs of air pollution was better than temperature, however, adherence to the reporting of the assessment results of risk of bias in all SRs (53.5% v. 34.3%) was inadequate. Conclusions Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs.

In the Industry 4.0 era, manufacturers commonly adopt digital technologies such as Internet of Things (IoT) and blockchain technology to improve information disclosure. In this paper, we focus on how the government can properly set an extended producer responsibility (EPR) policy (penalty or subsidy) and impose it on the manufacturer to entice the manufacturer to set the optimal pollutant emissions information disclosure (PEID) level which maximizes the social welfare. In the basic model, we build a simple consumer utility based stylized analytical model. We first derive from the government perspective the social welfare function and obtain the closed-form expression of the optimal level of PEID. Then, we explore the manufacturer’s problem on PEID under the EPR policy. We show how the EPR policy can be set so that the government can entice the manufacturer to set the PEID level which maximizes the social welfare. We also uncover the factors governing the EPR policy, and show the situations under which the EPR policy is in fact a subsidy scheme (i.e., the government sponsors the manufacturer). To derive more insights and check for robustness, we extend the basic model in a number of ways, namely (i) the situation when the manufacturer is risk sensitive, (ii) the case with the extended consumer responsibility (ECR) policy, (iii) the scenario when there is a per unit PEID level dependent operating cost (i.e., a variable cost), and (iv) the case when the platform is not supported by blockchain. We find that the form of EPR policy and the qualitative insights remain valid for the cases when the manufacturer is risk sensitive and there is a PEID level dependent operating cost. Interestingly, we uncover that, unlike the EPR policy, the use of ECR policy fails to help.

The assessment of greenhouse gases (GHGs) and air pollutants emitted to and removed from the atmosphere ranks high on international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need to consider the uncertainty in inventories of GHG emissions. The approaches to address uncertainty discussed in this special issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, system analytic perspective. They seek to strengthen the usefulness of national emission inventories under a compliance and/or global monitoring and reporting framework. The papers in this special issue demonstrate the benefits of including inventory uncertainty in policy analyses. The issues raised by the authors and featured in their papers, along with the role that uncertainty analysis plays in many of their arguments, highlight the challenges and the importance of dealing with uncertainty. While the Intergovernmental Panel on Climate Change (IPCC) clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of performing these studies go well beyond any suggestions made by the IPCC to date. Improving and conducting uncertainty analyses are needed to develop a clear understanding and informed policy. Uncertainty matters and is key to many issues related to inventorying and reducing emissions. Considering uncertainty helps to avoid situations that can create a false sense of certainty or lead to invalid views of subsystems. Dealing proactively with uncertainty allows for the generation of useful knowledge that the international community should have to hand while strengthening the 2015 Paris Agreement, which had been agreed at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). However, considering uncertainty does not come free. Proper treatment of uncertainty is demanding because it forces us to take the step from “simple to complex” and to grasp a holistic system view. Only, thereafter, can we consider potential simplifications. That is, comprehensive treatment of uncertainty does not necessarily offer quick or easy solutions for policymakers. This special issue brings together 13 papers that resulted from the 2015 (4th) International Workshop on Uncertainty in Atmospheric Emissions, in Cracow, Poland. While they deal with many different aspects of the uncertainty in emission estimates, they are guided by the same principal question: “What GHGs shall be verified at what spatio-temporal scale to support conducive legislation at local and national scales, while ensuring effective governance at the global scale?” This question is at the heart of mitigation and adaptation. It requires an understanding of the entire system of GHG sources and sinks, their spatial characteristics and the temporal scales at which they react and interact, the uncertainty (accuracy and/or precision) with which fluxes can be measured, and last but not least, the consequences that follow from all of the aforementioned aspects, for policy actors to frame compliance and/or global monitoring and reporting agreements. This bigger system context serves as a reference for the papers in the special issue, irrespective of their spatio-temporal focus, and is used as a guide for the reader.

[...]treated effluent monitoring has limited (if any) value for assessing and responding to public health conditions in the upstream community. WBS data are not useful for monitoring permit compliance, evaluating wastewater treatment processes, or ensuring receiving water protection. [...]this regulatory framework is inappropriate for WBS. [...]WBS programs rely on input from both public health departments and WRRFs to produce the most valuable data for local public health interventions at the most suitable locations. Since the early days of the COVID-19 pandemic, many WRRFs have willingly volunteered to collaborate with health departments to create successful WBS programs by contributing samples and other resources. [...]NPDES permit-based monitoring and reporting requirements typically apply either to the WRRF influent (which provides data at larger regional scales than upstream sampling) or to treated effluent (which is not useful for WBS due to degradation of markers throughout the treatment processes). [...]permit-driven monitoring would either not provide the data granularity needed for public health interventions or would generate confounded data due to the effects of wastewater treatment on opioids. Many small WRRFs have limited resources and are often located in rural areas, where opioid abuse has been especially pronounced [25]. [...]updating all WRRFs to be equipped for on-site opioid analysis is impractical.

Increasing greenhouse gaseous concentration in the atmosphere is perturbing the environment to cause grievous global warming and associated consequences. Following the rule that only measurable is manageable, mensuration of greenhouse gas intensiveness of different products, bodies, and processes is going on worldwide, expressed as their carbon footprints. The methodologies for carbon footprint calculations are still evolving and it is emerging as an important tool for greenhouse gas management. The concept of carbon footprinting has permeated and is being commercialized in all the areas of life and economy, but there is little coherence in definitions and calculations of carbon footprints among the studies. There are disagreements in the selection of gases, and the order of emissions to be covered in footprint calculations. Standards of greenhouse gas accounting are the common resources used in footprint calculations, although there is no mandatory provision of footprint verification. Carbon footprinting is intended to be a tool to guide the relevant emission cuts and verifications, its standardization at international level are therefore necessary. Present review describes the prevailing carbon footprinting methods and raises the related issues.

Wildfires release large amounts of greenhouse gases into the atmosphere, exacerbating climate change and causing severe impacts on air quality and human health. In this study, based on a bottom-up approach and using satellite data, combined with emission factor and aboveground biomass data for different vegetation cover types (forest, shrub, grassland, and cropland), the dynamic changes in CO2 emissions from wildfires in China from 2001 to 2022 were analyzed. The results showed that between 2001 and 2022, the total CO2 emissions from wildfires in China were 937.7 Tg (522.6–1516.0 Tg, 1 Tg = 1012 g), with an annual average of 42.6 Tg (23.8–68.9 Tg). The CO2 emissions from cropland and forest fires were relatively high, accounting for 45 % and 46 % of the total, respectively. The yearly variation in CO2 emissions from forest and shrub fires showed a significant downward trend, while emissions from grassland fires remained relatively stable. In contrast, the CO2 emissions from cropland fires showed an upward trend, primarily in Northeast China. Hot spot analysis and geographically and temporally weighted regression (GTWR) models revealed significant spatial heterogeneity in emissions across vegetation types. Persistent hot spots of shrub and forest fires were located in Southwest and South China, while Northeast China experienced sporadic but extreme fire events. The GTWR model for shrub fire CO2 emissions exhibited the highest predictive performance (R2= 0.87), and climatic factors (particularly temperature and humidity) were the main influencing factors. Notably, the recent rise in cropland fire CO2 emissions in Northeast China is closely linked to region-specific straw-burning policies. The research results provide valuable references for atmospheric transport models, regional fire management, and national carbon accounting frameworks in the context of climate change.

Corn production, and its associated inputs, is a relatively large source of greenhouse gas emissions and uses significant amounts of water and land, thus contributing to climate change, fossil fuel depletion, local air pollutants, and local water scarcity. As large consumers of this corn, corporations in the ethanol and animal protein industries are increasingly assessing and reporting sustainability impacts across their supply chains to identify, prioritize, and communicate sustainability risks and opportunities material to their operations. In doing so, many have discovered that the direct impacts of their owned operations are dwarfed by those upstream in the supply chain, requiring transparency and knowledge about environmental impacts along the supply chains. Life cycle assessments (LCAs) have been used to identify hotspots of environmental impacts at national levels, yet these provide little subnational information necessary for guiding firms’ specific supply networks. In this paper, our Food System Supply-Chain Sustainability (FoodS³) model connects spatial, firm-specific demand of corn purchasers with upstream corn production in the United States through a cost minimization transport model. This provides a means to link county-level corn production in the United States to firm-specific demand locations associated with downstream processing facilities. Our model substantially improves current LCA assessment efforts that are confined to broad national or state level impacts. In drilling down to subnational levels of environmental impacts that occur over heterogeneous areas and aggregating these landscape impacts by specific supply networks, targeted opportunities for improvements to the sustainability performance of supply chains are identified.